{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Stime</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Openstatus</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22271</td>\n",
       "      <td>22:54:04</td>\n",
       "      <td>114.167000</td>\n",
       "      <td>22.718399</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22271</td>\n",
       "      <td>18:26:26</td>\n",
       "      <td>114.190598</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22271</td>\n",
       "      <td>18:35:18</td>\n",
       "      <td>114.201401</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22271</td>\n",
       "      <td>16:02:46</td>\n",
       "      <td>114.233498</td>\n",
       "      <td>22.725901</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22271</td>\n",
       "      <td>21:41:17</td>\n",
       "      <td>114.233597</td>\n",
       "      <td>22.720900</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Num     Stime         Lng        Lat  Openstatus  Speed\n",
       "0  22271  22:54:04  114.167000  22.718399           0      0\n",
       "1  22271  18:26:26  114.190598  22.647800           0      4\n",
       "2  22271  18:35:18  114.201401  22.649700           0      0\n",
       "3  22271  16:02:46  114.233498  22.725901           0     24\n",
       "4  22271  21:41:17  114.233597  22.720900           0     19"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'D:\\Python\\Code\\Study2022\\交通大数据\\pygeo-tutorial\\data-sample\\TaxiData-Sample.csv',header=None)\n",
    "data.columns=['Num','Stime','Lng','Lat','Openstatus','Speed']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 应用str函数切割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Num</th>\n",
       "      <th>Stime</th>\n",
       "      <th>Lng</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Openstatus</th>\n",
       "      <th>Speed</th>\n",
       "      <th>hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22271</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>114.201401</td>\n",
       "      <td>22.649700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22271</td>\n",
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       "      <td>114.233498</td>\n",
       "      <td>22.725901</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>16</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22271</td>\n",
       "      <td>21:41:17</td>\n",
       "      <td>114.233597</td>\n",
       "      <td>22.720900</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Num     Stime         Lng        Lat  Openstatus  Speed hour\n",
       "0  22271  22:54:04  114.167000  22.718399           0      0   22\n",
       "1  22271  18:26:26  114.190598  22.647800           0      4   18\n",
       "2  22271  18:35:18  114.201401  22.649700           0      0   18\n",
       "3  22271  16:02:46  114.233498  22.725901           0     24   16\n",
       "4  22271  21:41:17  114.233597  22.720900           0     19   21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['hour']=data['Stime'].str.slice(0,2)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运用apply方法，相当于excel里面的函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'22'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tem = data['Stime'][0]\n",
    "def get_hour(tem):\n",
    "    return tem[0:2]\n",
    "get_hour(tem)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          22\n",
       "1          18\n",
       "2          18\n",
       "3          16\n",
       "4          21\n",
       "           ..\n",
       "1601302    20\n",
       "1601303    20\n",
       "1601304    20\n",
       "1601305    20\n",
       "1601306    20\n",
       "Name: Stime, Length: 1601307, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Stime'].apply(get_hour) ## 这里直接写函数名就行，不需要加括号"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运用pandas时间格式的内置函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tem = pd.to_datetime(data['Stime'])\n",
    "# tem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#tem.apply(lambda x :x.hour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "00    68745\n",
       "01    63142\n",
       "02    60680\n",
       "03    57494\n",
       "04    57060\n",
       "05    59636\n",
       "06    59742\n",
       "07    63780\n",
       "08    68224\n",
       "09    64026\n",
       "10    69309\n",
       "11    71286\n",
       "12    65876\n",
       "13    69212\n",
       "14    71510\n",
       "15    70241\n",
       "16    71798\n",
       "17    70333\n",
       "18    63514\n",
       "19    74179\n",
       "20    74938\n",
       "21    70069\n",
       "22    71503\n",
       "23    65010\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('hour')['Num'].count().rename('count')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  hour  count\n",
       "0   00  68745\n",
       "1   01  63142\n",
       "2   02  60680\n",
       "3   03  57494\n",
       "4   04  57060"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour_count = data.groupby('hour')['Num'].count().rename('count').reset_index()\n",
    "hour_count.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "                \"15:00\",\n",
       "                \"16:00\",\n",
       "                \"17:00\",\n",
       "                \"18:00\",\n",
       "                \"19:00\",\n",
       "                \"20:00\",\n",
       "                \"21:00\",\n",
       "                \"22:00\",\n",
       "                \"23:00\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"pcu/h\",\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"min\": \"0\",\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": true,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u8f66\\u8f86\\u5206\\u5e03\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_fb47f9ac19c2456c84e66de09bbdf299.setOption(option_fb47f9ac19c2456c84e66de09bbdf299);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x277f24b4bb0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyecharts.options as opts\n",
    "from pyecharts.charts import Line\n",
    "x_dd=[str(i) +':00'  for i in range(24)]\n",
    "mp = Line(init_opts=opts.InitOpts(bg_color='white'))\n",
    "\n",
    "#mp.add_xaxis(xaxis_data=list(hour_count['hour']))\n",
    "mp.add_xaxis(xaxis_data=x_dd)\n",
    "\n",
    "mp.add_yaxis(\n",
    "    y_axis=list(hour_count['count']),\n",
    "    series_name='车辆数',\n",
    "    label_opts=opts.LabelOpts(is_show=False)\n",
    "    )\n",
    "\n",
    "mp.set_global_opts(\n",
    "    title_opts=opts.TitleOpts('车辆分布'),\n",
    "    yaxis_opts=opts.AxisOpts(name='pcu/h',min_='0',splitline_opts=opts.SplitLineOpts(is_show=True)),\n",
    "    xaxis_opts=opts.AxisOpts(name='时间',axislabel_opts=opts.LabelOpts(horizontal_align='right'))\n",
    ")\n",
    "mp.render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "plt.plot(hour_count['hour'],hour_count['count'],'k-*')\n",
    "plt.bar(hour_count['hour'],hour_count['count'],width=0.5)\n",
    "plt.title('Hourly data Volume',)\n",
    "plt.xlabel('Hour',)\n",
    "plt.ylabel('Count')\n",
    "plt.ylim(0,80000)\n",
    "\n",
    "#保存图片\n",
    "plt.savefig(fname='Hourly data Volume.svg',format='svg',bbox_inches='tight')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Stime</th>\n",
       "      <th>SLng</th>\n",
       "      <th>SLat</th>\n",
       "      <th>ELng</th>\n",
       "      <th>ELat</th>\n",
       "      <th>Etime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:03:23</td>\n",
       "      <td>114.167465</td>\n",
       "      <td>22.562468</td>\n",
       "      <td>114.225235</td>\n",
       "      <td>22.552750</td>\n",
       "      <td>00:10:48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22223</td>\n",
       "      <td>01:01:14</td>\n",
       "      <td>114.135414</td>\n",
       "      <td>22.575933</td>\n",
       "      <td>114.166748</td>\n",
       "      <td>22.608267</td>\n",
       "      <td>01:08:17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0       22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1       22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2       22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3       22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4       22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "\n",
       "      Etime  \n",
       "0  00:10:48  \n",
       "1  00:15:19  \n",
       "2  00:29:06  \n",
       "3  00:54:42  \n",
       "4  01:08:17  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TaxiOD = pd.read_csv(r'D:\\Python\\Code\\Study2022\\交通大数据\\pygeo-tutorial\\data-sample\\TaxiOD.csv')\n",
    "TaxiOD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiOD['Hour'] = TaxiOD['Stime'].apply(lambda x :  x[:2])\n",
    "ODcount = TaxiOD.groupby(['Hour'])['VehicleNum'].count().rename('Count').reset_index()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z7uoYbTrjfdvZCliKt7/CQcAe/vOPds5tiWqTjrcjdh/gR+BdYDfgYLz9F/o4576s8IciIiIiIiJVEs9ziE4GPnLObY48aWYj8TZ2G29mHfxdpiONd85NqMwDzGwH4Em8vSIGO+cm+ufb4m0gONrMXomxm/oEvGToIefcKL9NKt4GiqcA1+Ht4xDpRrxkaA5wrL8vA2Z2FXCfH8eRlYlbREREREQqJ26HzDnnZkcnQ/75R/E2iWsLdKvhYy4EWgBTSpIh/xk/A3/wi6MjG5jZwUA/vI32/hDRphC4FK/3KtdPkEraNAEu94uXlSRDfrv78XrBjjCznjX8PCIiIiIiEiFuE6LtKNmVvaCG9znRP8baeftVYDPQP2peUEmbqdHD4vxE6l0gG29X7hL9gJbAF865hTGeVfL8AVULX0REREREKpJwCZGZnQ3shbfB3/IYVXLM7C9m9riZ3bidXpcD/OOC6AvOuQK8+UHpePOWttsm6vz+NWwjIiIiIiI1FM9ziAAws2vwFlNoBuztv/8BON05VxSjydlR5TvM7GXgvMihav5GgS394vflPP57vMUSdsUb1gbeErLba4PfpkR12pTLzD4p51IXYBPwXWXuIyIiIiISJzoCG51z7araMO4TIuA4vFXfSnwDnOOc+yiq3grgauA1v042cDgwFhiMt3DCKRH1MyPebyzn2fn+sXmMdnXdpjpS0tLSmnfq1Kmmc6tERERERBqML774gi1btmy/YgxxnxA55/oDmFkWsB9wMzDLzG50zv0pot6/oprmA8+YWR6wBBhoZn0SYVNA59w+sc6b2SedOnXq9skn5XUgiYiIiIjEn3322Ydly5ZVaxRUwswhcs6tcc69C5wAfIQ3FK5XJdr9CDzlF38XcWlDxPuMcpo384/rY7Sr6zYiIiIiIlJDCZMQlXDObcXbSNWo/KpsJYsv7BRxn3XAWr/YoZx2Jee/iTj3bT21ERERERGRGkq4hMj3q39sXcn62f4xP+r8Iv/YI7qBmTUG9sVbevvzyrSJOr844lx12oiIiIiISA0lakJ0hH/8YnsVzcwoXUwhetnrV/3jkBhNT8Jbcnt61AaxJW0GmFla1LPaAocBq4HZEZdm4/VGdTKz7jGeVfL8qeV/EhERERERqaq4TIjMrJ+Z/c7MUqLONzazK/CW1t6EN3QOM2ttZpeZWfOo+pnAY0Bv4CdgYtSjxgPrgJPNbFBEuzZ4q9MB3BfZwDk3Dy/BaQPcE9EmFXgUaAw85A/tK2lTADziF8eZWbOIdlfh7T80K8bKeSIiIiIiUgPxusrcnngLIfxqZh8BvwGt8FaZ2wlvGNt5zrmSlSaa4SUcY8xsPvAj3nC6HsCOwBpgiHOuzLLXzrlVZjYceAF4ycxm+s/qD2QB9zvnZsaI73xgDjDKzHKAZUAvYA/gfeDuGG3u9O/bF1huZu/i7TvUG/gFGF7pn46IiIiIiFRKXPYQAbOAu4DP8HpPhgL9gFXAw8B+zrkXIur/htdb8xHe5qSD/fo/4fXw7OucixzCFnDOvYy3X9EbwIF4q9itwEu4RpfTZrlfdwJe4nUKUAzcARztnNtmkXR/2N1Rfp2NwEC8hGgC0MM59+X2figiIiIiIlI15pwLOwapJ2b2Sbdu3bQPkYiIiIgkFH8fomXl7cdZkXjtIRIREREREakxJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStJQQiYiIiIhI0lJCJCIiIiIiSUsJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNJSQiQiIiIiIklLCZGIiIiIiCQtJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStJQQiYiIiIhI0lJCJCIiIiIiSUsJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNJSQiQiIiIiIklLCZGIiIiIiCQtJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStJQQiYiIiIhI0lJCJCIiIiIiSUsJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNJSQiQiIiIiIklLCZGIiIiIiCQtJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStJQQiYiIiIhI0lJCJCIiIiIiSUsJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNKK24TIzK4ys4lmttzM1prZFjP7xsz+YWb7VdDuPDObZ2YbzGyVmU0zs77beVY/v94qv908MztnO206mNlTZvaDmW02s8/N7DYzS6+gTVMzu92vu9lv+6SZ7bz9n4iIiIiIiFRV3CZEwPXA8cAq4G3gVWAzcDbwkZmdFN3AzP4CPAXsC0wH5gHHAO+Y2cBYDzGzwcAs4HfAYuB1YE/gaTO7t5w2nYGFwHnAb8AUoBFwMzDdzNJitEkHZgA3AZl+m++A84GFZrZHxT8OERERERGpqnhOiE4Gsp1zvZ1zg/zXXsBlQGNgvJmlllQ2s/7AKLwE5QDn3EDn3O+Aw4Ei4Ckzy4p8gJntADyJl8wMcc4d6ZwbAnQFVgCjzezIGLFNAFoBDznn9nPODQP2AiYB/YDrYrS5EegDzAG6OOeGOed6A6OB1n4cIiIiIiJSi+I2IXLOzXbObY5x/lHgC6At0C3i0lX+8U7n3PKI+nOAx4Es4IKo210ItACmOOcmRrT5GfiDXxwd2cDMDsZLelZG1ME5VwhcCmwFcqOStSbA5X7xMufchoh29+P1TB1hZj1j/SxERERERKR64jYh2o6t/rEAvLk5QI5/7qUY9UvODYg6f2IFbUqG6PWPmhdU0maqc25LZAM/kXoXyAYOjbjUD2gJfOGcW1iF+EREREREpAYSLiEys7Pxhqct91/45TTgF+fc9zGaLfCP+0edPyDqesA5VwAsBdKBLpVpU8GzqtNGRERERERqKHX7VRo2M7sG2AdoBuztv/8BON05V+RX28U/xkqGcM7lm9kaINvMmjvn1ptZC7xem3Lb+ecPAnbFG9a23WdFnN814lx12oiIiIiISA3FfUIEHAccHVH+BjjHOfdRxLlM/7ixgvvk480jag6sj2hTUbt8/9i8Cs+qrTblMrNPyrnUqTLtRURERESSRdwPmXPO9XfOGd68nMPxhsnNMrMbwo1MREREREQaukToIQLAObcGeNfMTsBbuvoOM3vTOTcfKFm1LaOCWzTzj+v944aIaxnAukq0iWxX3rNqq025nHP7xDrv9xx1i3VNRERERCQZxX0PUTTn3FbgecAoXZXtW//YIVYbM2uGN1xutXNuvX+fdcDaitpFnP8m4lyFz6rFNiIiIiIiUkMJlxD5fvWPrf3jZ8AWoLWZ7Ryjfg//uDjq/KKo6wEzawzsi7f09ueVaVPBs6rTRkREREREaihRE6Ij/OMXAM65TcAM/9zQGPWH+MepUedfjboe6SS8JbenR20QW9JmgJmlRTYws7bAYcBqYHbEpdl4vVGdzKx7FeITEREREZEaiMuEyMz6mdnvzCwl6nxjM7sCOBvYhDd0rsT9/vFGM9szos0hwAhgDfD3qEeNx5s7dLKZDYpo0wYY6xfvi2zgnJuHl+C0Ae6JaJMKPAo0Bh7yh/aVtCkAHvGL4/whfCXtrsLbf2hW1Mp5IiIiIiJSQ/G6qMKewFPAr2b2EfAb0ArYD9gJbxjbec6570oaOOemm9mDwCjgYzN7C2gCHIM33+h8f2EGItqsMrPhwAvAS2Y2039Wf7w5R/c752bGiO98vIUdRplZDrAM6AXsAbwP3B2jzZ3+ffsCy83sXbx9h3oDvwDDK//jERERERGRyojLHiJgFnAX3tyg/fGGwfUDVgEPA/s5516IbuScuxIvWfkULxE6BJgOHO6cmxzrQc65l/GW834DOBA4AViBl3CNLqfNcr/uBLx5TKcAxcAdwNHOuS0x2mwGjvLrbAQG4iVEE4AezrkvK/h5iIiIiIhINZhzLuwYpJ6Y2SfdunXr9skn5e3bKiIiIiISf/bZZx+WLVu2rLztZyoSrz1EIiIiIiIiNaaESEREREREkpYSIhERERERSVpKiEREREREJGkpIRIRERERkaSlhEhERERERJKWEiIREREREUlaSohERERERCRppYYdgIiIiIiIVF1RURHvvPMOAIcffjiNGjUKOaL4pIRIRERERCQOLViwgJycHADmz5/PQQcdFHJE8UlD5kRERERE4tCsWbNivpeqUUIkIiIiItLAOefIz88v88rLywuuz5gxY5vrzrkQI44fGjInIiIiItLALV68mO7du5d7fdq0aWRmZpY5t2jRIvbff/86jiz+qYdIRERERKSBmzJlSpXbTJ48ufYDSUBKiEREREREGrjc3FwGDRpU6fqDBw8mNze3DiNKHEqIREREREQauO5jZvNh5/Np0e90MCu/YkojsvuPYH6n8+g+Znb9BRjHNIdIRERERKSBK/jla9bOfZGNn74LFSyWYKlNSO/QDasoaZIy1EMkIiIiItJAzZ07l5NPPpkfn7ycjctmgSuusL4r2MSP/7yG/P++V08Rxj/1EImIiIiINCDOOaZPn87dd99dZmntWDK69MXh2PT5nNKTRQX8OmUMW385neLi40lJUR9IRfTTERERERFpAIqLi5k0aRIHH3wwxx577DbJUOPWu5WZP5TdfwStBl5H64HXk91/xDb3W/v+swwdOpQNGzbUdehxTT1EIiIiIiIh2rp1K88++yxjxozh008/3eZ63759WdH+GNL3OAhXsJGVL9xC1lHDSe/QLajToucAmrTtxKq3Hqe4YDNFa34AYOLEiaxYsYL//Oc/7LrrrvX2meKJeohERERERGpJUVEReXl55OXlUVRUVGHdTZs28eijj7Lnnnty7rnnbpMMHXfcccyaNYv33nuPpp16YWakpDWj3dn3lkmGSqR36Eb78x+i/XkPkL57z+D84sWL6dWrF++9p3lFsSghEhERERGpJQsWLCAnJ4ecnBwWLlwYs866deu455572H333bnsssv45ptvgmtmxpAhQ/jwww95/fXXOfzww6u8YlxKWjPaDLmZFr1OCc798ssv5OTkMH78+Op9sASmhEhEREREpJbMmjUr5nvwkpKbbrqJXXbZhWuvvZaff/45uJaamsp5553HsmXLePHFF+nZsyc1YSmNyM65gAkTJtCkSRPAG5p30UUXMWrUKAoLC2t0/0SihEhEREREpBqcc+Tn55d5RS6EMGPGDPLz8/nss8+47LLL2GWXXbjzzjtZu3ZtUCc9PZ0rrriCL774gqeeeoquXbvWaoznnnsuM2fOpG3btsG5hx56iBNOOIHVq1fX6rPilRZVEBEREZGEVlRUxDvvvAPA4YcfTqNGjWrlvosXL6Z79+7lXp82bRqZmZkxr7Vo0YLLL7+cUaNG0aZNm1qJpzyHHHII8+fPZ+DAgSxYsACAt956i4MPPpipU6fWehIWb9RDJCIiIiIJrTLzeqpjypQpVW6TkZHBXXfdxbfffsuf/vSnOk+GSnTs2JF3332XU089NTi3YsUKevfuzWuvvVYvMTRUSohEREREJKFVNK+nqrZu3cry5cuZNm0aTZo0YY899qh02+7du7N8+XKuu+46WrZsWaM4qiMjI4PnnnuOO+64Izi3bt06TjrpJO677z6cc/UeU0OgIXMiIiIikjCcc2zcuLHMueh5PZdcckmZ6xkZGWVWctu6dStfffUVK1asYPny5WWOX3/99XaX096GpZB91AWsOuj/6PvQQr4e077qH6yWmBk33ngj++yzD2effTb5+fkUFxdz9dVXs2TJEh5//HHS09NDiy8MSohEREREJGFUZ17PH/7wB/Lz84PE55tvvql60lOBlocMo0Wvk2vtfrXhlFNO4f333+f//u//gmW/n376aT777DMmTZpEu3btQo6w/ighEhEREZGEUZ15PWPHjq1SfTNj1113pXPnzsz9rQmpzVuzbt5Eijevj1l/w+I3aXnIqVhq4yrHVpf2339/5s+fz+DBg3n33XcBmDt3Lr169WLKlCn06NEj5Ajrh+YQiYiIiEjCyM3NZdCgQTW+T0pKCrvvvjvHHHMMI0eO5P7772fq1Kl8+umnbNq0ia+++oq33nqLHY8dCa64TDKU0aUvGV36BuWiDb+xbv6kGsdUF1q3bs306dO56KKLgnPff/89hx56KM8//3xwrqioiLy8PPLy8mq196whUA+RiIiIiCSMrKwsPux8Ptn9W7N6xt+huKINSI1GLdvQOLs9F5zYl86dO7PnnnvSuXNndtttN9LS0ir1zDXv/Tt4n91/BM17nATA+gWvsHr6E0GdloecGrN92Jo0acITTzzBfvvtx+9//3uKiorYtGkTp512GkuXLuW2224LVuoDmD9/PgcddFDIUdceJUQiIiIiklDMjBY9B1C8cS1r338uZp3MHieyQ86FWCNvGNsjY06s9vM6jnqWlS/cQtZRw0nv0C0436LnAJq07cSavCdpc+pt1b5/fTAzrrjiCvbee29OPfXUYNPWO++8k08++YSePXsGdWfNmpVQCZGGzImIiIhIwnGFBWxY/Ga51zd9PhdqaZXplLRmtDv73jLJUIn0Dt1od/a9pKQ1q52H1bH+/fszd+5cunTpEpybNGkS99xzT1CeMWMG+fn5ZV7xvGS3EiIRERERSTjr5k2iaMOqMuea7FT6S35DntcTtk2bNvH555+XObd+fekcqZKV+iJfS5Ysqe8wa40SIhERERFJOJHzegAaZe9M2zPHkt1/RLl1xFOdlfomT55c+4HUEyVEIiIiIpJwdjrvoTLlrEOGktIolRY9B9D2zLGkte9Kx9xnQoquYavqSn2DBw8mNze3DiOqW0qIRERERCThbFj0WvC+UYs2NOt2ZFCOt3k99a37mNn+Sn0jIKWCNdhSUsnuP4L5nc6j+5jZ9RdgLVNCJCIiIiIJpWjDatYvKl1QoWXvwVgjLa5cFSUr9bXsM6TcOi37DKVFzwGYWT1GVvuUEImIiIhIQlk3fxIUbQWgUbNsMvc/JuSI4tP2VurbsPhNXOHWeoyobighEhEREZGEsWrVKtZ/XDpcrnmvU7DUJiFGFL+iV+rL6NKXjC59g3KirNSnvkMRERERSRgPPfQQrmATACnpzWl+4PEhRxS/Ilfhy+4/guY9TgJg/YJXWD39iaBOy0NODSW+2qKESEREREQSwvr163noodLV5Zof9H+kNGkaYkTxreOoZ1n5wi1kHTW8zKazLXoOoEnbTqzJe5I2p94WYoS1QwmRiIiIiCSExx57jNWrVwNgTZrSvOeAkCOKbylpzWh39r0xr5Ws1JcINIdIREREROLepk2buO+++4Jy8x4n0ig9M8SIJF4oIRIRERGRuDd+/HhWrlwJgKWm0eKggeEGJHFDQ+ZERESkzhQVFfHOO+8AcPjhh9OoUaOQI5JEVFBQwNixY4Ny5gHH0qhZVngBSVyJyx4iM8sws4Fm9ncz+8zMNptZvpktMrObzWyb/lEzu9XMXAWvMRU8r5+ZTTOzVWa2wczmmdk524mxg5k9ZWY/+PF9bma3mVl6BW2amtntft3NftsnzWznqv2EREREGoYFCxaQk5NDTk4OCxcuDDscSVD//Oc/+f777wFo3LgxLQ4eHHJEEk/itYfoDOBv/vtPgf8ALYC+wG3A6WZ2hHNuZYy2s4EVMc5/FOtBZjYYeB4veXwH+BU4GnjazPZ3zl0do01nYA7QClgKvAscBNwMHG1mRzvntkS1SQdmAH2AH4EpwG7A+cBJZtbHOfdlzJ+GiIhIAzVr1qwy7w866KAQo5FEVFhYyJgxpd9rn3vuubzVolWIEUm8icseImAr8Fegm3Oum3PuVOfc74C9gIVAV+Av5bQd75w7L8brxeiKZrYD8CTQCBjinDvSOTfEv/8KYLSZHRnjGRPwkqGHnHP7OeeG+bFNAvoB18VocyNeMjQH6OKcG+ac6w2MBlr7cYiIiDRYzjny8/PLvPLy8oLrM2bM2Oa6cy7EiCURvPjii6xY4X3XnZKSwh//+MeQI5J4E5cJkXPuaefcCOfcp1HnfwQu84uDzKym2xJfiNfzNMU5NzHiOT8Df/CLoyMbmNnBeEnPyog6OOcKgUvxkrlcM0uNaNMEuNwvXuac2xDR7n5gMXCEmfWs4ecRERGpM4sXLyYzM7PMa9q0acH1adOmbXN9yZIlIUYs8a64uJi77rorKJ922ml07tw5xIgkHsVlQrQdi/xjGrBjDe91on98Kca1V4HNQP+oeUElbaZGD4vzE6l3gWzg0IhL/YCWwBfOuVgDrEuer8X0RUSkwZoyZUqV20yePLn2A5GkMXXqVJYuXRqUr7su1iAckYolYkK0h3/cCqyKcT3HzP5iZo+b2Y3b6XU5wD8uiL7gnCvAmx+UDnSpTJuo8/vXsI2IiEiDkpuby6BBgypdf/DgweTm5tZhRJLInHP86U9/CsoDBw5k3333DTEiiVeJmBCN8o+vR/fQ+M7264wA7gA+NLOXolemM7MWeL02AN+X86yS87tGnNulntqIiIg0KI0aNWJWwR40br1bxRVTGpHdfwTzO51H9zGz6yU2STzTp09n/vz5QfmGG24IMRqJZ/G6ylxMZnYCcAFe79BNUZdXAFcDrwHf4A1bOxwYCwzGWzjhlIj6kQnSxnIeme8fm8doV9dtymVmn5RzqVNl2ouIiFTW+vXrmTp1Ki+++CKvvfYaW7bE+i4yioNGTVtgZnUfoCSsyN6hY489VisYSrUlTEJkZl2BfwEGXOOcWxR53Tn3r6gm+cAzZpYHLAEG+ktbz62XgEVEREJS081Sq5UERXJF/Dr1zxT8/AVZR5xbtbYiwOzZs8ss6a7eIamJhEiI/I1LX8fr9bnfOfdgZds65340s6fweo9+B5QkRBsiqmUA62I0b+Yf10ecK2mXUc4ja6tNuZxz+8Q67/ccdavMPUREJHGVbJYKMH/+/Ep9s16SBL3wwgu8/vrr5SZBKemZNGrZlq0/fxGcy+jSF4CNn79fpu66eRMpWPkVq649lB122KG6H0eSUGTv0KGHHsrhhx8eYjQS7+J+DpG/V9CbePNrShKbqlruH3cqOeGcWwes9YsdymlXcv6biHPf1lMbERGRaoneLLU869ev55lnnmHgwIG0bt2aM888kylTpmyTDGVnZzN8+HBef/11Olz+L7au/Kr0Wv8RtBp4Ha0GXkd2/xHbPGPz1wvp1auXlt+WSluwYAGvvfZaUL7xxhtDjEYSQVz3EPkLIbyG1+sxEbjIVW+Ht2z/mB91fhHePKMewLKoZzcG9sVbevvzqDYn+21iKTm/OKpN5LXKtBEREdku5xwbN5adohq9Weoll1wSlNetW8fbb7/NSy+9VGFP0A477MApp5zC0KFDycnJoXHjxgBY3qt0HPUsK1+4hayjhpPeoXRgQoueA2jSthOrZ4ynUbNsNq34AIAvv/ySQw45hAkTJjBkyJBa++ySmCL3HerZsyfHHntsiNFIIojbhMjM0oApwMHAG8DpzrmiatzHKF1MIXrZ61fxEqIhePOTIp2Et+T2K865zVFtbgYGmFla5Ep3ZtYWOAxYDUQuqzMbrzeqk5l1d859HPWskn8dplbho4mIiLB48WK6d+9e7vWSzVIro7wkKFpKWjPanX1vzGvpHbqx0zn345xj3QcvsWbWPwBHfn4+Q4cO5brrruOOO+6o8rwmSQ6ffvopEydODMo33HCDFueQGovLIXNm1gh4FsjB2+h0kL8vUHn1W5vZZWbWPOp8JvAY0Bv4Ca+XKdJ4vLlDJ5vZoIh2bfBWpwO4L7KBc24eXoLTBrgnok0q8CjQGHjIObc1ok0B8IhfHGdmzSLaXYW3/9As59xH5X1GERGRWKqzWWqkHXbYgQsuuIA33niDn376ifHjx3PccceVmwxVlpnRss9Q2gy5hZYtWwbn7777bgYMGMCaNWtqdH9JTHfffTclg4H22WcfTj755JAjkkQQrz1El1Paq/Mr8Gg53w5c7Zz7FW9RgkeAMWY2H/gRaI03FG1HYA0wxDlXZkyBc26VmQ0HXgBeMrOZwG9AfyALbwGHmTGeez4wBxhlZjl4w+164W0a+z5wd4w2d/r37QssN7N38eZF9QZ+AYZX9AMRERGJJTc3l0WLFpX5Vn17srKyGDJkCEOHDuWoo46qcfJTkaadDuK9u+YzcOBAli3zRqe/9tprHHzwwUyePJlu3bQWkHi+/PJLnnnmmaB83XXXkZISl9/tSwMTr3+LsiPenwKcW86rZAzAb3i9NR8BXfD2HeqH1yt0H7Cvcy7mznDOuZfxhs29ARwInIC3p9F5zrnR5bRZ7tedgJd4nQIU420Ee3SsDWP9YXdH+XU2AgPxEqIJQA/n3JcV/UBERERiycrK4sPO53sLGqRU/D1ok/ZdaT30NlpcOIG//e1vHHvssXWaDJXYc889mTt3LgMHDgzOLV++nN69ezN58uQ6f77Eh7Fjx1JU5M2O2GOPPRg2bFjIEUmiiMseIufcrcCtVai/Hri2Bs+bDRxfxTbf4fUUVaXNJrz5RzdXpZ2IiEhFzIwWPQdQvHEta99/LmadFocMI/vws+s5slLNmzfn5Zdf5k9/+hM33+z9M7hhwwZOOeUUbr75Zm655Rb1BiSx//3vfzz11FNB+dprryU1NS5/jZUGSP9nERERSQKusIANi98s93r+kum4wq3lXq8PKSkp3HTTTfznP/+hefPSab+33347AwcOZO3atRW0lkR23333UVDgTRffeeedOeecc0KOSBKJEiIREZEksG7eJIo2rCpzrkn7rsH7og2/sW7+pPoOK6YBAwYwb9489tprr+Dc1KlT6d27N5999lmIkUkYfvnlFx5//PGgfM0115CWlhZiRJJolBCJiEhcKSoqIi8vj7y8vGA+gWzfmvf+Xaac3qkX7c76c5nNUqPrhKlr16588MEHnHTSScG5zz77jIMPPpipU7ULRTL5y1/+wqZNmwBo3bo1F110UcgRSaJRQiQiInFlwYIF5OTkkJOTw8KFC8MOJ25kHvC74L2lNmGH/iOCuUVtzxxLWvuudMx9poI71L+WLVsyZcqUYE4ReBvH/t///R933HEHxcXFIUYn9WHNmjU88sgjQfn3v/89GRkZIUYkiUgJkYiIxJVZs2bFfC/lK1j5FRsWvR6UW/QZSuOsdkE5vUM32p19LylpzWI1D1VKSgq33XYbEydOLLOB7M0338yQIUNYv359cE69h4ln3LhxrFu3DvAS5JEjR4YckSQiJUQiItJgOefIz88v88rLywuuz5gxY5vrJZs2isc5x6q3HgPn9aakZu1Ey96DQ46q6k455RQ++OADOnfuHJybNGkSffr0Yfny5YB6DxNNfn4+DzzwQFC+4oorymziK1JblBCJiEiDtXjxYjIzM8u8pk2bFlyfNm3aNteXLFkSYsQNzz//+U+2fL8sKO9wzCVYapMQI6q+bt26MX/+fI4/vnQnjGXLltGrVy9ee+019R4mmL/+9a/89ttvAGRkZDBq1KiQI5JEpYRIREQarClTplS5jTbyLLVmzRquueaaoNy0yyE03aNniBHVXFZWFlOnTuXaa0u3F1y7di0nnHAC48ePD86p9zC+bdmyhXvvvTcoX3LJJbRq1SrEiCSRKSESEZEGKzc3l0GDBlW6/uDBg8nNza3DiOLLTTfdxMqVKwGw1DR2yEmM1bkaNWrEaaedts35yCW51XsY3yZMmMAPP/wAQFpaGqNHjw45IklkSohERKTBysrK4sPO55N99MVg2/kny1J446sC9rvplfoJroFbsGABjz76aFBu2XcYqS3bhBhR7VLvYeIqLCzknnvuCcrDhw+nffv2IUYkiU4JkYhIgkqUFbdc4RYKfv4iWBSg/IrFbFgwlf89cSHXXnstq1atqrh+AisuLuayyy4LlqVO3WFnWvQ6JeSoapd6DxPXs88+y1dffQV4vYF/+MMfQo5IEp0SIhGRBJUIK2599tln/PSP0eQvfbviimbBW7d1C/fccw+77747t912W7BkbzJ56qmnmDt3blDeof8lWGrjECOqfUHvYf8RkJJafsWURmT3H8H8TufRfczs+gtQqqW4uJi77747KJ911lnstttu4QUkSUEJkYhIgor3Fbeef/55DjroILb++s021zK69CWjS9/SE86RvsdBpGSULsm7bt06br31VnbffXfGjh3Lxo0b6yPs0K1atYo//vGPQXno0KE03f3AECOqOyUby7bsM6TcOum796RFzwFYRNIsDdekSZP49NNPAe/P97rrrgs5IkkGSohERBJAIu3Xs2XLFi6//HJOO+00NmzYsM317P4jaDXwOloNvM7rHfBt/nohO48YT9bh55CVlRWcL0kQ9thjDx5++GG2bNlSHx8jNNdff32wVHGzZs24//77Q46obrnCAjYsfrPc65u/mMeWn5bXY0RSXc45/vSnPwXlIUOGsNdee4UYkSQLJUQiIgkgUfbr+frrrznssMMYN25ccM7SmrHjSaNJa9+VtmeODb7tL+kdaHvmWNLad6Vj7jOkNGlKy0NO5auvvuLGG28kMzMzuM/PP/9Mbm4ue+65J+PHj2fr1q1hfMQ6NX/+fP76178G5VtuuYUOHTqEGFHdWzdvEkUbSueLZXTpS/oeB5Wps/KFWyku2FzfoUkVvf7662WG915//fUhRiPJRAmRiEgCSIQVt1555RV69OjB/Pnzg3MHHnggO533IJn7HEW7s+8lvUO3bdqld+hGu7PvJSWtWXAuKyuLO+64gy+//JLRo0eTnp4eXPvuu++46KKL6NatG//+97/jesGJSEVFRYwcOTLo+evWrRtXXnlluEHVgzXv/Tt4X9J72GbILWR2L928tXjTWlZNfyKM8KSSonuHTjzxRLp37x5eQJJUlBCJiCSAeF5xq7CwkGuvvZYBAwawevXq4PyIESN4//33aZzVrtr3bt26Nffeey9ffPEFI0eOpHHj0oUFVqxYwVlnncUBBxzAxIkTYw4hjKeV+saPH8+HH34YlMeNG1fm8yaqjqOejdl7uONxl5HR7YigXv6St9jwSV4Fd5L6FvnfV15eHrNnly56ccMNN4QYmSQbJUQiIgmg0ituAU067MP7Wf0bxIpbP/74I0cffXSZPUcyMjL417/+xeOPP16mZ6cm2rdvz7hx4/j8888ZPnw4KSml//x98sknDB48mIMOOojXXnutTGIULyv1/frrr2Umn59xxhkceeSR4QVUj1LSmpXbe9jqhN/TpH3pHJRVbz7K559/Xp/hSQUi//uK/Pt71FFHccghh4QYmSQbJUQiIgmiMituARR8/wk//v1Sfn7xFt58883QFleYMWMG3bt355133gnOdevWjfnz53PmmWfWyTN32203/v73v/Ppp59y2mmnlbm2YMECTjjhBA477DBmzpwJxM9Kfddee23Qu9a8eXP+/Oc/hxxRw2CNUmn9f38MhlO6gk0MGzaMzZs1n6ghiPxvat68ecF79Q5JfVNCJCKSQLa34lakzV9+xHHHHcc+++zDE088UW/LUhcXF3PnnXdyzDHHsHLlyuD8WWedxbx58+jWbdtv+mtbly5dePbZZ1m0aBEnn3xymWuzZ8/mqKOOIicnh0mTJgXnG+pKfXPnzuXvf/97UL7tttto3759iBE1LKkt27DjCaOC8scff8zVV18dYkTJaXsrYZY46KCD6N27d4P570uSgxIiEZEEEr3ilqfs/iuRe/UAfPrpp1xyySV06NCBa6+9lu+++67O4vv111858cQTuemmmyguLgYgLS2NJ554gn/84x80a9ZsO3eoXfvvvz+TJ09m3rx52wzRycvL4/333w/KDXGlvpKFFErst99+XHHFFSFG1DBldOlL854DgvK4ceOYOHFiiBEln+2thFniww8/pHnz5g3ivy9JHkqIREQSSOSKWyXSOx9cZr+e4s0baDPsTpp26lWm3urVq7nnnnvYfffdGTZsGHPmzKnVb2jnzp1Ljx49eP3114Nze+yxB++//z4XX3xxqBtn9urVi9/97ndVbhf2Sn2PP/54mblN48aNIzW14jlkySr7yOE0adspKA8fPpyvvvoqxIiSSyKshCmJSwmRiEgC6TjqWZrstBekNgnOZXY7cpv9epru1p02Q27h888/54orrijTM1NUVMQLL7xA37596dOnD8888wwFBQUVPrei1dicczz44IMcdthhZXqfBg4cyEcffUSPHj1q6dPXTLyt1Pfzzz+XmWtxzjnncNhhh4UWT0NnqY1p9X9/CPamWrt2Laeddtp2/25L7Yi3/74kuSghEhFJIClpzWjRexAUer/kWZOmNO3s9QTF2q9nzz335KGHHuL777/nvvvuY7fdditzv3nz5nHmmWey++67c9ddd/Hrr7/GfG55q7GtXbuWoUOHcuWVV1JYWAhAamoq9913HxMnTiQrK6sWP33NVGWlvmb7H8P8TueFulLfH//4R9auXQtAy5YtGTt2bGixxIvGO+xcZuPaefPmaQJ/Pan0f18pqWT3HxH6f1+SXJQQiYgkmI3LSlduytizDymNt790dVZWFldddRUrVqxg4sSJHHHEEWWu//DDD9xwww107NiRiy66iKVLl5a5Hms1to8//piDDjqIl19+Obi28847M3PmTK666qpQh8iVp7Ir9eUvfovVb/8NV7S1niIr67333uPpp58OynfeeSdt27YNJZZ4c/rpp3PhhRcG5XvvvTfmXBapfZX576tln6HBflIi9UUJkYhIAineks/GL+YH5WZ7H1FB7W01atSIU045hZkzZ7Jw4ULOO+88mjQpHX63efNmxo8fz3777cdRRx3FSy+9xPr168usFjVjxgzGjRtH7969WbFiRXD+2GOPZeHChfTr168Gn7DuVXalvvUf/Yef/n0t3377bT1EVaqwsLDMQgrdu3fnkksuqdcY4t2DDz7IPvvsE5TPOeccvv/++xAjSh7b++9rw+I3cYXhfNEgyUsJkYhIAtn4+Rzwey1SmrYgfbfu1b5X9+7deeqpp/j222+59dZbt+mBmDlzJkOHDqVFixZlvmGfNm0al19+eZm5GZdeeinTpk2jdevW1Y6nvkSv1JfRpS8ZXfrGrFvw42cceOCBvPrqq/UVHo888kiZ1bceffRRLaRQRRkZGTz//PM0bdoUgN9++40zzjgjGNYpdSfWSpiNWpb+v6Vow2+smz8puplInVJCJCKSQPIjh8t1PRRrVPNflNu2bcstt9zCN998w9NPP027du2qfI927drRqFGjGsdSHyJX6svuP4JWA6+j1cDryqzUF7mU+apVqzjppJO4/vrr6/wX6h9//JGbb745KA8fPnyb5cKlcvbZZx8eeeSRoPzuu+9y++23hxhRcoi1EmbrU24o899XrDoidUkJkYhIgijKX83mbxYF5WbdqjZcbnvS0tI455xzWLZsGYcffnil28XbalEdRz1LWvuutD1zbDCXoWTuQ7BS35XPseOJv8dS04J2d999N/379+fHH3+ss9iuvvpq1q9fD0B2djZjxoyps2clg/PPP58zzzwzKN95553MmDEjxIgSX8dRz5LStHQvtLQO3Uhru8c2K2GK1CclRCIiCSL/v++B8zY7bdS8NWk7710nz8nOzubrPtf4q0VV0OuT0iguV4tKSWtGu7PvJb1Dt22uRa7Ul7nv0bQ75z66du0aXJ81axbdu3evk1+qZ86cyTPPlP6ieNddd8XFEMSGzMx47LHH2HPPPQFvifgzzzyTn3/+OeTIEpcrLsJt3RSUmx94YvA+1kqYIvVBCZGISIKIXF2uWbfDMau7/8WXrhY1tNw6LfucmvCrRTVpvRvz58/n9NNPD86tXLmSY445hjvvvJPi4uJaec7WrVu57LLLgvJBBx3ERRddVCv3TnbNmzfn+eefDxYP+emnnzj77LNr7c9OytqweDrO3xYgJSOLjL1iz88TqU9KiEREEsBXX33Flh/+G5Rre7hcLFotypOZmcm///1vHnvsseCX6uLiYm666SZOOOGEcvduqooHH3yQZcuWAV4y+uijj8bNnKx4cOCBB3L//fcH5bfeeot77rknxIgSk3PFbPi4dAGWzAOOwxo1DjEiEY8SIhGRBPDcc88F7xvv2JHGrXev82dubzW2ZFotysy45JJLmDNnDrvvXvqzf+ONNzjwwAN5//33q33v77//nltvvTUoX3TRRfTq1asm4UoMI0eOZNCgQUH5pptuYvbs+BnqGQ82f7WQwjU/eQVLoXn348INSMSnhEhEJAFEzi3J6HZEvQxTq8xqbMm2WlSPHj1YsGABAwcODM59//33HHHEEdx///0456p8z9GjR5Ofnw/AjjvuyF133VVb4UoEM+Pvf/87u+66KwBFRUWcfvrp/PbbbyFHljjWLyxdnr5p54NJbdEmxGhESikhEhGJc0uWLGHp0qVBudnelV8BriYqtRpbEq4WlZWVxcSJE7nvvvuC/YEKCwsZPXo0gwYNYs2aNZW+1/Tp03nhhReC8pgxY9hxxx1rO2TxZWVl8dxzzwV/bt999x3Dhw+vViIrZX399ddsWlG6aXTkYgoiYVNCJCIS55599tngfZOdutA4u329PLeyq7ElIzPjqquuYtasWXTo0CE4P3nyZHr06MFHH3203Xts2bKFyy+/PCj37t2b4cOH10m8UqpPnz7cfffdQfk///kPDz30UIgRJYYnnngC8BLL1Oz2pO92QLgBiURQQiQiEsecc2USomZ71/1iClJ5ffv2ZeHChRx3XOlcia+++oq+ffvy+OOPl+l5KCoqIi8vj7y8PIqKinjggQf47LPPAEhJSeHRRx8lJUX/bNeHq666ihNOOCEoX3PNNXz44YchRhTftmzZwvjx44Ny8wNPqNNVMEWqSn8bRUTi2Ny5c/n666/9kpGx92FhhiMxtGrVimnTpnHHHXcECU1BQQGXXnopZ511Fhs2bABgwYIF5OTkkJOTw2uvvcYdd9wR3OPSSy+lR48eocSfjFJSUnj66adp397rbd26dSvDhg1j7dq1IUcWn1566aVgtUVLTaPZfv1DjkikLCVEIiJxLHIxhfRd9yM1c4cQo5HypKSkcOONN/LWW2/Rpk3pRPJnnnmGXr16sXTpUmbNKt1H6vrrr2fjxo0AtG7dukxyJPWjVatWPPPMM0ES++WXX3LxxRdrPlE1jBs3LnjfrNsRNErPDDEakW0pIRIRiVOFhYVlJtxn7H1keMFIpeTk5PDxxx9z2GGlPXn//e9/6dWrF//617+Cc0uWLAne33nnnTRp0kS/iIfgiCOOKLPk+QsvvMDf/va3eo8jejhlPFm4cCFz5swJypkHnlBBbZFwKCESEYlTM2bMYOXKlQA0adJEO77HiZ122okHHnigzLnNmzezaNGimPVHjBhBZmZmmSRJ6s/1119PTk5OUB41ahSLFy+u1yQlcjjlwoUL6/RZte2xxx4L3jfZaS/S2nUOMRqR2JQQiYjEqcjhcscff7yGocSRV199dfuVokyePLn2A5HtatSoEf/6179o3bo14CWvw4YN47333qu3JCVyOGXk+4ZuzZo1/PvfpXuRNe+h3iFpmJQQiYjEoU2bNjFx4sSgfMYZZ4QYjVRVbm4ugwYNqnT9wYMHk5ubW4cRSUV22mmnMkMa//vf/zJ69OigXJtJinOO/Pz8Mq+8vLzg+owZM7a53lCHUz799NPBXLgdd9yRZl216Is0TEqIRETi0LRp01i/fj0AmZmZnHTSSSFHJFWRlZXFh53PJ7v/CEhJLb9iSirZ/Ucwv9N5dB8zu/4ClDKcc/Tr169MEhS5l1RtJimLFy8mMzOzzGvatGnB9WnTpm1zvSEOp3TO8eijjwblCy64AEttEmJEIuWr4P/CIvWjqKiId955B4DDDz+cRo0ahRyRSMMXuffQwIEDycjICDEaqQ4zo0XPARRvXMva95+LWadln6G06DmgniOTaIsXL6Z79+7lXi9JUiItWrSI/fffP2b94uJi1q9fz+rVq1m9ejVr1qwJ3kculFJZkydPLvdZYZkxYwaff/454P1dHzFiBM//9dOQoxKJTQmRhK5ksijA/PnzOeigg0KOSKRhW7t2La+88kpQPv3000OMRmrCFRawYfGb5V7fsPhNWh5yKpbauB6jkmhTpkypcpuRI0fSrVu3mEnP2rVrKS4urpXYGupwysjeoeOPP5499tgDUEIkDZMSIgld9GRRJUQiFZs0aRJbtmwBvHH5xxxzTMgRSXWtmzeJog2rgnJGF2+lwI2fvw9A0YbfWDd/Ei0POTWU+MSTm5vLokWLyszb257Zs2cze3YdDnNMaUR2zoXM73QS3cfM5usxJ9bds6ro+++/L5NEXnbZZSFGI7J9SoikXjnnggmWJaIni15yySVlrmdkZGBm9RKfSDyIHC536qmn0rixeg/i1Zr3Slfgyu4/guY9vLlg6xe8wurpTwR1lBCFq3TOV2tWz/g7FBfW2r2bN29OdnY22dnZZGVlkZ2dzVtfbCAlrRkFP3/Blu+WxmzXdM8+DXY45V//+tdgKfLdd9+d4447LuSIRCoWlwmRmWUAxwIDgEOBXYEiYAXwMnC/c25DOW3PA0YC3YACYC5wp3Pu/Qqe1w+4AegDNAGWAY845/5RQZsOwB3AccAOwLfAs8DdzrnN5bRpClwHnAbsAqwCXgducs79r7xnxZPaHoctkmx+/vlnpk+fHpQ1XC6+dRz1LCtfuIWso4aT3qFbcL5FzwE0aduJNXlP0ubU20KMUEpUZs5XSrMdaNJ6V1LSM0lJa8ZlvzugTKIT/b5ly5akpm77q9hu176KKyzgf09cWG48mz5/n8INq0nNzK61z1gbCgoKymxee8kll2husDR4cZkQAWcAJf+1fQr8B2gB9AVuA043syOccysjG5nZX4BRwCbgTSAdOAY41syGOOcmRz/IzAYDz+OtyPcO8CtwNPC0me3vnLs6RpvOwBygFbAUeBc4CLgZONrMjnbObYlqkw7MwEu6fgSmALsB5wMnmVkf59yXlf8RNUzVGYfdECeLioTlxRdfDOYedOzYkX79+oUckdRESloz2p19b8xr6R26lXtNwrG9OV9mRpvBNwdzvsbUYBhbrOGUxQWb2Py1v+eRc/zy8u3sdO4D5dwhHJMnT+ann34CIC0tjeHDh4cckcj2xeuy21uBvwLdnHPdnHOnOud+B+wFLAS6An+JbGBm/fGSod+AA5xzA/02h+P1Lj1lZllRbXYAngQaAUOcc0c654b4918BjDazI2PENwEvGXrIObefc26YH9skoB9eL1C0G/GSoTlAF+fcMOdcb2A00NqPI+5p7w2RmoncjPW0004jJSVe/zcuEn9iJSkl876gdM5XbYgeTtlq4HW0OfV2mnY+ODhf8NNyNpczpC4s48aNC94PGzaMVq1ahRiNSOXE5b+kzrmnnXMjnHOfRp3/ESiZuTfIzCIXvL/KP97pnFse0WYO8DiQBVwQ9agL8XqepjjnJka0+Rn4g18cHdnAzA7GS3pWRtTBOVcIXIqXzOWaWWpEmybA5X7xssjhfs65+4HFwBFm1jPmDySOdB8zu8p7b2RlZdVbfCIN2VdffcWcOXOCsjZjFalfsZKUVgOv8/5Ni1GnJjqOepa09l1pe+ZYWvQcgJlhZrQeeD2NstoF9X57/RFcYUGtPLOmli5dGmyjAd5KeyLxIC4Tou1Y5B/TgB0hmJuT459/KUabknPRsxNPjLoe6VVgM9DfH+4W3WZq9LA4P5F6F8jGm/tUoh/QEvjCObewCvHFpZJx2C37DCm3TsneG1pMQaTUc8+Vzlvo2rUrBxxwQIjRiCSf8pKUFj0H0PbMsaS170rH3Ge2f6NKKBlOGTm3DMAapdJ6wDWA9+9j4arvWTvnxVp5Zk099thjwfsePXpw8MEHV1BbpOFIxIRoD/+4FW9RAvCGq6UBvzjnvo/RZoF/jJ6ockDU9YBzrgBvflA60KUybSp4VnXaxLXK7L3hCrfWY0QiDV/kcLkzzjhDXxiI1LPykhQonfOVktaszuNIa78XzSNWmFs790U++eSTOn9uRdavX88//lG61tTIkSP1/yiJG4mYEI3yj69H9NDs4h9jJUM45/KBNUC2mTUHMLMWeL025baLOL9rxLkKn1WLbcplZp/EegGdKtO+PkSPwyY1rcz12hyHLZIIlixZwtKlpXMFtLqcSHLLOuwsGjVv7RWKC7n44otrbbPX6vjnP//Jhg3eiP+srCz9P0riSkIlRGZ2At48oK3ATRGXStZx3rhNo1L5/rF5VJuK2kW3qcyzaqtNXNtm740YeynU1jhskUQQufdQr1696Ny5c4jRiEjYUtIy2OG40jk677//Pk888UQosTjnePTRR4Py+eefT0ZGRiixiFRHwiREZtYV+BfeoNprnHOLttMkYTnn9on1Ar4IO7YS0eOwM/c5ssz1xm32qLVx2CLxzjlXJiHSYgoiApDRqRcZXQ8Lytdeey3/+1/9b1v47rvvlhmyF73BukhDlxAJkZntjLeBaTbepqwPRlUpWbWtoq8rSgb9ro9qU1G76DaVeVZttYlr0eOwm7TejcatdwuuZ3Q5pF7GYYvEg7lz5/L1118D3qIkp556argBiUiDscPRFwf/Xq5bt44rrrii3mOI7B065phj6NKlSwW1RRqeuE+I/L2C3sSbX/MUsM1GqcC3/rFDOfdohrfs9mrn3HoA59w6YG1F7SLOf1PZZ9Vim4TTrNsRwfv8ZbNwzoUYjUjDEbmYwlFHHUX79u1DjEZEGpJGmdlkHVW6+emkSZOYNKn+5uD++OOPvPzyy0H5sssuq6C2SMMU1wmRmWUCrwHdgInARS72b9GfAVuA1n5vUrQe/nFx1PlFUdcjn90Y2Bdv6e3PK9OmgmdVp03Cabb34cH7wlXfU/BzgxnhJxKawsJCXnjhhaCsicoiEi1z/2M54ojSLxUvv/xy1q5dW0GL2jN+/HgKCwsB6NixIyeeeOJ2Wog0PLWaEJnZLn6PzfbqZZvZLturt517pAFTgIOBN4DTnXNFseo65zYBM/zi0BhVSjbEmRp1/tWo65FOwltye7pzbnOMNgP8GCNjbgscBqwGZkdcmo3XG9XJzLpXIb6EktqyLWk7ly5lunHZrBCjEWkYZsyYwcqVKwFo3LgxgwcPDjkiEWlozIwnnniCtDTv144ffviB66+/vs6fW1hYWGYhhxEjRpCaWsGm6yINVG33EH0F/LkS9cYCX1b3IWbWCHgWb7PVd4FB/r5AFbnfP95oZntG3OsQYATestt/j2ozHlgHnGxmgyLatPE/A8B9kQ2cc/PwEpw2wD0RbVKBR4HGwEPOua0RbQqAR/ziOH8IX0m7q/D2H5rlnPtoO58x7pUZNvfpLFxxzBxXJGlELqZw/PHHk52dHWI0ItJQ7bXXXtx4441B+bHHHuP999+v02dOnTo1WMShcePGXHjhhXX6PJG6UtsJkVGydXLl6lbX5cAp/vtfgUfNbEKMV6uSBs656cCDwI7Ax2Y22cymAe8AqcD5zrk1kQ9xzq0ChgPFwEtmNsPMXsQbgtcZbwGHmTHiOx/4DRhlZovN7Dm/zSDgfeDuGG3uBD4A+gLLzex5M5uLl3D94seR8DK6Hgrm/bUs2rCKLd+Hu9GcSJg2b97MxIkTg7JWlxORivzhD39gn332AbzVKS+66CIKCrb3fXH1jRs3Lng/ZMgQ2rZtW2fPEqlLYc0hagVsqkH7yK9ITwHOLecVuZcQzrkr8ZKVT4FjgEOA6cDhzrnJsR7knHsZOBxvWN6BwAnACuA859zoctos9+tOAFr7MRYDdwBHR2wYG9lmM3CUX2cjMBBvoYgJQA/nXLV71OJJo4yWpO9+YFDO17A5SWLTpk1j3bp1ADRr1owBA7bdr0tEpESTJk3429/+hpn3nfOyZcu45557ttOqev773//y9ttvB+WRI0dWUFukYavxQE8zOzzqVLsY5yKftxdwHFDtr/6dc7cCt1az7QS8JKMqbWYDx1exzXd4yVdV2mwCbvZfSatZtyPZ/KU3OnDjZ7PZsmVLMC5aJJlEri43cOBAbXQoItt1yCGHcOmllwZLYd95552ceuqp7LXXXrX6nMcffzx4v99++9GvX79avb9IfaqNHqKZQJ7/Ai/ZySvn9RYwDkgjau6NSImMzr2xVC8BKt68gTfeeCPkiETq39q1a3nllVeCsobLiUhl3X333ey8s7eobkFBARdffDHFxcW1dv/8/HwmTJgQlEeOHBn0SonEo9pIiP4R8QL4Iupc5Gs8cBvQyzn3XC08WxJQSloGTTsfHJQjvyUXSRaTJ09myxZvdO2OO+7IMcccE3JEIhIvWrRowSOPPBKU33nnHZ588slau/8zzzwTLOvdvHlzzjrrrFq7t0gYajxkzjl3Xsl7MzsXeM85lxQLAEjdadbtSDb+910A/vOf/7B+/XqaN28eclQi9Sfyi4ChQ4fSuHHjEKMRkXgzcOBABg0aFCzMcs0113DSSSfRrl27Gt3XORcMxwM499xzyczMrKCFSMNXq4sqOOdSlAxJbWi6Rw9S0r3/wW7atIkpU6aEHJFI/fn555/LTFbWZqwiUh0PP/wwLVq0AGDNmjWMGjWqxvecO3cuH3/8cVC+9NJLa3xPkbCFtcqcSIWsUWMy9iqdoKlhc5JMXnzxRYqKvD24OnTowKGHHhpyRCISj9q3b19mlbkXXnihzNzE6ojsHTryyCPp1q1bBbVF4kOtJ0RmlmZm55nZk2b2mr93T6zX29u/mySzyE1a33zzTX755ZcQoxGpP5GbsZ5++umkpOi7KxGpnosvvrjMCnAjR45k/fr11brXypUreeGFF4LyZZddVuP4RBqCWv1X1sx2BhYBfwfOw1tx7sgKXiLlSuuwD40ydwSgqKiIF198MeSIROreV199VWZ3eQ2XE5GaSElJ4a9//WswD/G7777jxhtvrNa9nnzyyWCj15122omTTz651uIUCVNtf+34Z6ALMAcYAuwP7F7Oa49afrYkGEtpRLO9S7e00rA5SQbPPVe6AGfXrl3p3r17eMGISELo1q0b1113XVB++OGHmTdvXpXuUVRUVGbvoYsvvliLvUjCqO2E6DjgW6C/c26ic26pc+6b8l61/GxJQBkRw+Zmz57N119/HV4wIvUgeric9vYQkdpw/fXXB5uzOue48MIL2bp1a6Xbv/baa3zzjferW6NGjbjooovqJE6RMNR2QpQGfOCc21zL95Uk1aRtpzK7a0d+ey6SaJYuXcqSJUuCsobLiUhtSUtL429/+1tQXrJkCffdd1+l248bNy54P3DgwGDjV5FEUNsJ0RKgVS3fU5KYmXHGGWcEZQ2bk0QW2Tt00EEHseeee4YYjYgkmsMOO4yLL744KN92222sWLFiu+2++OILXn/99aCsxRQk0dR2QnQPcLiZHVzL95UkFvkt+ZIlS8p8gy6SKJxzZRL+yC8CRERqyz333BNszrp582ZGjBiBc67CNpFzh7p27cqRRx5ZlyGK1LvaTogWAPcDb5vZbWZ2qJntZma7xHrV8rMlQe2555706tUrKEd+iy6SKObOnRvMkTMzhg0bFm5AIpKQsrKyePjhh4PyjBkzePrpp8utv2nTJp588smgPHLkSM1tlIRT2wnR18DVQDPgRmAW8AXwVYzXl7X8bElg0cPmtvdtlki8iUz0jzzySNq3bx9iNCKSyAYPHsyAAQOC8ujRo1m5cmXMus8//zyrVq0CoFmzZpxzzjn1EqNIfarthOgd/zUr4n15r3dr+dmSwIYNGxZ8I/XNN98wZ86ckCMSqT2FhYU8//zzQVnD5USkLpkZ48aNIzMzE4BVq1bx+9//PmbdRx99NHh/1lln0bJly3qJUaQ+1WpC5Jw70jl3VGVftflsSWw77bQTOTk5QVmLK0giycvLC76dbdy4MYMHDw45IhFJdB07duSuu+4Kys8880yZhRMA5s+fz/z584PypZdeWm/xidSn2u4hEqkzkd+av/DCC1XaP0GkIYtM8I8//niys7NDjEZEksXIkSPp3bt3UL700ktZt24deXl55OXl8cgjjwTX+vXrxwEHHBBGmCJ1TgmRxI1BgwbRpEkTAH755RfefvvtkCMSqbnNmzczceLEoKy9h0SkvjRq1Ii//e1vpKamAvD1118zcuRIcnJyyMnJKbP338iRI8MKU6TOpdbmzczs5ipUd865O2rz+ZLYsrKyOPHEE5k0aRLgfav+u9/9LuSoRGpm2rRprFu3DvAmLEdOdBYRqWv77bcff/jDH4Lhc5E91gUFBQC0adNGQ3klodVqQgTcCjigvPUYS5YGM/+9EiKpkjPOOCNIiCZNmsTGjRvJyMgIOSqR6ov85ePkk0+mWbNmIUYjIsnEOcfGjRv5/e9/z3PPPceXX34ZcxXXc845h8LCQgoLC8nIyNCy25JwanvI3PnAcP8Y+boAL1l6Hy8ZGufXE6mSE088kebNmwOwYcMGXnnllZAjEqm+devWlfk7rNXlRKQ+LV68mMzMTFq3bs2XX5a/G8q9995LZmYmmZmZ2hxdElJtrzL3dDmvp5xztzvnDgOuxUuSFtTmsyU5NG3alEGDBgVlrTYn8WzSpEls2bIFgB133JFjjz025IhEJJlMmTKlym0mT55c+4GIhKzeF1Vwzo0Fvgfu2l5dkVgiv0WfNm0aq1evDjEakaopKioKVnCKTOiHDBlC48aNQ4xMRJJNbm5umS8Zt2fw4MHk5ubWYUQi4QhrlbklwKEhPVviXE5ODm3atAFg69atvPzyyyFHJFJ5CxYsCFZwmj59enBew+VEpL5lZWXxYefzye4/AlIqmFaekkp2/xHM73Qe3cfMrr8ARepJWAlRJ2p/QQdJEqmpqQwbNiwoa9icxJNZs2YF74uLiwHo0KEDhx6q74hEpP6ZGS16DqBlnyHl1mnZZygteg7QYgqSsOo1ITKzbDO7D+gOzKvPZ0tiifw2febMmfzvf/8LMRqR2Jxz5Ofnl3nl5eVtU2/QoEFs2rSJ/Pz8mCs8iYjUJVdYwIbFb5Z7fcPiN3GF2gxdEletJkRm9mUFr5XAr8DvgU3AdbX5bEkuvXv3Zvfddwe8Xzqff/75kCMS2VbJCk6Rr2nTpm1T76GHHtIKTiISmnXzJlG0YVVQzujSl4wufYNy0YbfWDd/UhihidSL2u4h2q2CV0vgO+CfQC/nnHqIpNrMrEwvkYbNSUOkFZxEJB6see/fwfvs/iNoNfA6Wg28zptbFKOOSKKp7WW3Uyp4pTnndnPOneucW1abz5XkFJkQffTRR3z22WchRiOyLa3gJCLxoOOoZ0lr35W2Z44N5gqVzC1qe+ZY0tp3pWOuvniUxBXWogoiNdatWzcOOOCAoPzss8+GGI3ItrSCk4jEg5S0ZrQ7+17SO3Tb5lp6h260O/teUtKahRCZSP2o84TIX0ghu66fI8kpeticJqRLQ6MVnERERBq2OkmIzOwEM3vDzDbgLaTwq5ltMLPXzeyEunimJKfTTjsteL98+XI++uijEKMRiU0rOImIiDRctZ4QmdkDwFTgGCADWAes9d8fC0w1s/tr+7mSnHbZZRcOO+ywoKzFFaQhil7BKZpWcBIREQlPbS+7PQwYBfwC5ALZzrls59wOQBZwBbASGGVmp9bmsyV5RQ6be+655ygqKgoxGpFtlbc6U9YR5263joiIiNSt2u4hGglsBg53zj3inFtbcsE5t845Nw44Atji1xWpsSFDhpCa6k1Y//HHH5k1a1bIEYmUVbKCU/MeA4JzTdp1pmWfoVrBSUREJGS1nRAdAMxwzn1eXgX/2gygey0/W5JUq1atOO6444Kyhs1JQ1OyglPh2p+Cc+m7eiskagUnERGRcNV2QtQEyK9EvXy/rkitOP3004P3L730Elu2bAkxGpFtueIiNn+3NCiXJEQiIiISrtpOiL4AjjCzcr/qNLMMvGFzX9TysyWJnXzyyTRt2hSAtWvX8tprr4UckUhZBT9+jivY5BVSUkmLsd+HiIiI1L/aToheANoAk81sz+iLZtYJmAi0Bp6v5WdLEsvMzOTkk08Oyho2Jw3Npm8WBe/Tdu5KSuP0EKMRERGRErWdEN0LLACOBpaZ2Qdm9rz/mgv8F2/p7Y+A+2r52ZLkIlebmzp1KuvWrQsxGpGyNkckRBouJyIi0nDUakLknNsEHAmMAwqAXsBQ/3Wwf24ckOPXFak1xx13HNnZ2QBs3ryZyZMnhxuQiK9462a2/O/ToKyESEREpOGo9Y1ZnXMbnHNX4A2LOwI43X8dAbR2zl3hnNtQ288VadKkCUOHDg3KGjYnDcWW7z+FokIArElT0nbqEnJEIiIiUqLGCZGZ5ZjZOWZWZoawc26jc+5d59zz/utdYDe/7lE1fa5ILJHD5qZPn87PP/8cYjQins3fRgyX67AP1ig1xGhEREQkUo0SIjPrCLwK3Ah8V4km3wE3AK+YWfuaPFsklsMOO4ydd94ZgKKiIl588cWQIxLR/CEREZGGrKY9RBfi7Sf0B+fc+u1V9utcAzQFLqjhs0W2kZKSUmZPIg2bk7AVb95AwU+luwyk76aESEREpCGpaUJ0DPCLc25yZRs45/4D/AwcX8Nni8QUOWxuzpw5fPnllyFGI8lu87dLwBUDkNK0BY1b7xZuQCIiIlJGTROirsD8arT7ENirhs8Wial79+507do1KD/33HMhRiPJLnq4nFmtr2UjIiIiNVDTf5mbAWur0W4tkFmTB5tZTzO71swmmtn3ZubMzFVQ/9aSOuW8xlTQtp+ZTTOzVWa2wczmmdk524mvg5k9ZWY/mNlmM/vczG4zs3J3YzSzpmZ2u193s9/2STPbuXI/FQEwszK9RP/+979xrty/GiJ1SvOHREREGraaJkSrgbbVaNfWb1sTNwF3A6cAVUkYZgNPx3h9FKuymQ0GZgG/AxYDrwN7Ak+b2b3ltOkMLATOA34DpgCNgJuB6WaWFqNNOjDD/1yZfpvvgPOBhWa2RxU+Y9KLnEe0bNkylixZEmI0kqx++OEHtv5Wut6MEiIREZGGp6Zrvy4D+phZ08putGpmGcAhwLwaPnsOXoIy3399DWyTaMQw3jk3oTIPMLMdgCfxkpnBzrmJ/vm2wHvAaDN7xTk3M6rpBKAV8JBzbpTfJhV4AS+Buw64NarNjUAf/3MdW7JXk5ldBdznx3FkZeIW6Ny5MwcffDDz5nl/zZ555hn233//kKOSZDNjxozgfaMWbUjNahdiNCIiIhJLTXuIXsEbNndjFdrciLfK3NSaPNg5d49z7mbn3FTn3E81uVcFLgRaAFNKkiH/2T8Df/CLoyMbmNnBQD9gZUQdnHOFwKXAViDXT5BK2jQBLveLl0VuXOucux8v8TvCzHrW3kdLfJHD5p599lmKi4tDjEaS0dtvvx289+YPWYjRiIiISCw1TYgex1sx7lozu9EqmC1sZilmdhNwrd/miRo+uz6c6B9finHtVWAz0D9qXlBJm6nOuS2RDfxE6l0gGzg04lI/oCXwhXNuYYxnlTx/QNXCT26nnnoqKSneX8lvv/2W999/P+SIJJk458okRE213LaIiEiDVKOEyDm3ERgMbAFuA74ys3vN7AwzO8Z/neHPtfkKb5jYZrzhZxtrGHt15ZjZX8zscT+Jq6jXpeQ3mAXRF5xzBcBSIB3oUpk2Uecjx29Vp41sx0477UROTk5Q1p5EUp9WrFjBd99FzB/aRQmRiIhIQ1Tj9V+dc+8DffHmE3UEfg/8E2/xgdf991f515YBhzjn5tT0uTVwNjAKGAHcAXxoZi+ZWZlV78ysBV6vDcD35dyr5PyuEed2qac2UgmRw+ZeeOEFtm7dGmI0kkwie4cat9qFRpnZIUYjIiIi5amVDTGccx875/YDTgAeBd4HPvNf7wPjgBOcc/s55xaVf6c6tQK4GtgHbxW3jsCZwP/wern+GVU/MkEqrzcr3z82j9GurtuUy8w+ifUCOlWmfSIZNGgQaWneWhu//fYbb731VsgRSbKInj8kIiIiDVNNV5krwzlX0ivU4Djn/hV1Kh94xszygCXAQDPr45ybW//RSV1p2bIlJ554IhMnemtiPPPMM5xwwgkhRyWJrri4mLy8vKCcvmv38IIRERGRCiX9lunOuR+Bp/zi7yIubYh4n1FO82b+cX2MdnXdplzOuX1ivYAvKtM+0UQOm5s0aRLTpk0jLy+PoqKiEKOSRLZo0SJ+++03r2AppO+yb7gBiYiISLmSPiHyLfePO5WccM6tA9b6xQ7ltCs5/03EuW/rqY1U0gknnECLFi0A2LhxIyeeeCI5OTksXBhrQT+RmoscLtek3Z6kpDWroLaIiIiESQmRp2S2c37U+ZL5Tj2iG5hZY2BfvFXzPq9Mm6jzi2vYRiqpadOmDBo0aJvzs2bNCiEaSQZl5g9puW0REZEGLekTIvN2SjzFL0Yve/2qfxwSo+lJeEtuT3fObY7RZoCZpUU9qy1wGLAamB1xaTZeb1QnM+se41klz6/RZrbJxjlHfn4++fn5nHLKKdtcnzFjRnC95OWcCyFSSSQFBQW88847QVkLKoiIiDRsSZEQmVlrM7vMzJpHnc8EHgN6Az8BE6OajgfWASeb2aCIdm2AsX7xvsgGzrl5eAlOG+CeiDapeCvwNQYecs5tjWhTADziF8eZWbOIdlfh7T80yzn3URU/elJbvHgxmZmZZGZmcvLJJ29zfdq0acH1kteSJUtCiFQSyQcffMDGjd6Ckenp6aTvvHfIEYmIiEhFanWVufpkZicCN0WcauKfj1wl7g7n3Kt4ixI8Aowxs/nAj0BrvKFoOwJrgCHRm8U651aZ2XDgBeAlM5sJ/Ab0B7KA+51zM2OEdz4wBxhlZjl4+y/1AvbAW4b87hht7vTv2xdYbmbv4u071Bv4BRi+vZ+JlDVlypQqt5k8eTL776/9b6X6IofL9evXjxWpTUKMRkRERLYnnnuIWuMlCyUv889Hnmvtn/sNr7fmI6AL3r5D/fB6he4D9nXORQ5hCzjnXgYOB94ADsTba2kFcJ5zbnQ5bZb7dSf4MZwCFONtBHu0c25LjDabgaP8OhuBgXgJ0QSgh3Puy+39QKSs3NzcmHOHyjN48GByc3PrMCJJBpEJ0dFHHx1iJCIiIlIZcdtD5JybgJcsVKbueuDaGjxrNnB8Fdt8h9dTVJU2m4Cb/ZfUUPcxs3Gdzye7f2tWz/g7FBfGrpiSSnbOBczvdBJZWVn1GqMklg0bNjB3bmkn9dFHH80TE38JMSIRERHZnnjuIRLZLjOjRc8BtOwTa10MT8s+Q2nRcwDe+hoi1ffuu+9SWOgl3i1btqRnz54hRyQiIiLbo4RIEp4rLGDD4jfLvb5h8Ru4wq3lXheprMjhckceeSSNGjUKMRoRERGpDCVEkvDWzZtE0YZVQTl9twMpnXIGRRtWsW7+pBAik0Sj+UMiIiLxRwmRJLw17/07eJ/dfwRtTr2d5gefUm4dker49ddf+fjjj4OyEiIREZH4oIRIEl7HUc+S1r4rbc8cG8wVyup7Go0ydwjqpO/eI8QIJRHk5eUF73faaSf23lv7D4mIiMQDJUSS8FLSmtHu7HtJ79At4lwGWUeWLgK4+Yv5bPpqYRjhSYKIHC6Xk5OjRTpERETihBIiSVrNuh1J2s6l3+KvfvuvbN2qxRWkejR/SEREJD4pIZKkZWZk9x9ByQILW3/7jkceeSTcoCQuffvtt6xYsSIoKyESERGJH0qIJKmltetMZvfjgvKtt97Kzz//HGJEEo8ie4c6d+7MLrvsEmI0IiIiUhVKiCTpZR12NinpmQCsW7eO6667LuSIJN5ouJyIiEj8UkIkSa9RRkuyDjsrKD/11FN88MEHIUYk8cQ5x4wZM4KyEiIREZH4ooRIBMjsfjyNW+8WlK+44gqKi4vDC0jixn//+19+/PHHoHzUUUeFGI2IiIhUlRIiEcBSGrFD/xFBef78+UyYMCG8gCRuRA6X6969O61atQoxGhEREakqJUQivvRd9mPYsGFB+dprr2XNmjXhBSRxQfOHRERE4psSIpEIf/7zn8nIyADgl19+4fbbbw85ImnIioqKmDlzZlBWQiQiIhJ/lBCJROjYsSPXX399UH744YdZtmxZiBFJQ7ZgwYKgFzE1NZXDDjss3IBERESkypQQiUQZPXo0e+yxBwCFhYXk5ubinAs5KmmIIofL9e7dm8zMzBCjERERkepQQiQSJT09nQceeCAov/3220yaNCnEiKSh0vwhERGR+KeESCSGAQMGcNxxxwXlq666ik2bNoUYkTQ0mzdv5r333gvKSohERETikxIikRjMjAcffJDU1FQAvvnmG8aOHRtyVNKQzJkzh82bNwOQkZFBnz59Qo5IREREqkMJkUg59tprL6688sqgPGbMGL755pvwApIGJXK43GGHHUaTJk1CjEZERESqSwmRSAVuuukm2rVrB3hDpEaPHh1yRNJQaP6QiIhIYlBCJFKBFi1acM899wTll19+ucwvwpKc1q1bx/z584OyEiIREZH4pYRIZDvOOussDjnkkKCcm5vL1q1bQ4xIwjZr1iyKiooA2GGHHejevXu4AYmIiEi1KSES2Y6UlBQefvhhzAyAZcuW8eijj4YclYQpspfwqKOOIiVF/ysVERGJV/pXXKQSevbsyYUXXhiUb7nlFlauXBliRBImzR8SERFJHEqIRCrpT3/6E1lZWQCsXbuW66+/PtyAJBQ///wzS5cuDcpKiEREROKbEiKRSmrdujW33357UH7yySfLTKyX5DBjxozgfYcOHdhzzz1DjEZERERqSgmRSBVceuml7LPPPgA457jiiisoLi4OOSqpT9HD5UrmlomIiEh8UkIkUgWpqak8/PDDQfmDDz7gn//8Z4gRSX3T/CEREZHEooRIpIqOOuoohg4dGpT/+Mc/snbt2hAjkvry5Zdf8vXXXwdlJUQiIiLxTwmRSDXce++9NG3aFPAm2d9xxx0hRyT1IbJ3qGvXrrRv3z7EaERERKQ2KCESqYZddtmF6667Lig/+OCDfPrppyFGJPVBw+VEREQSjxIikWq6+uqr2W233QAoLCzkyiuvxDkXblBSZ4qLi8usMKeESEREJDEoIRKppqZNm/LAAw8E5TfffJP//Oc/IUYkdWnp0qX88ssvAKSkpHDkkUeGG5CIiIjUCiVEIjVw8sknc8wxxwTl3//+92zatCnEiKSuRA6X69GjB9nZ2SFGIyIiIrVFCZFIDZgZDz74IKmpqQB89dVX3HfffSFHJXVB84dEREQSkxIikRrae++9yc3NDcp33XUX3377bYgRSW3bunUrs2bNCspKiERERBKHEiKRWnDLLbfQtm1bADZt2sQ111xDUVEReXl55OXlUVRUFHKEUhPz589nw4YNADRp0oR+/fqFHJGIiIjUFiVEIrWgRYsWjBkzJii/8MIL/PWvfyUnJ4ecnBwWLlwYYnRSU5HD5fr27UtGRkaI0YiIiEhtUkIkUkvOOeccevfuHZRvv/324H3kcCuJP5o/JCIikriUEInUAuccmzZtYuzYscG5n376KXg/Y8YM8vPzy7y0Z1F82LhxI3PmzAnKSohEREQSS2rYAYgkgsWLF9O9e/dyr0+bNo3MzMwy5xYtWsT+++9fx5FJTc2ePZuCggIAmjdvTq9evUKOSERERGqTeohEasGUKVOq3Gby5Mm1H4jUusjhckcccUSwxLqIiIgkBiVEIrUgNzeXQYMGVbr+4MGDyyzVLQ2X5g+JiIgkNiVEIrWg+5jZfNj5fLL7j4CUCnoQUlLJ7j+C+Z3OIysrq97ik+pZvXo1H330UVBWQiQiIpJ4lBCJ1BIzo0XPAbTsM6TcOi37DKVFzwGYWT1GJtU1c+bMYPGLNm3asO+++4YckYiIiNQ2JUQitcgVFrBh8ZvlXl+/8FVc4dZ6jEhqInK4XE5OjhJZERGRBBS3CZGZ9TSza81sopl9b2bOzLa7jrGZnWdm88xsg5mtMrNpZtZ3O236+fVW+e3mmdk522nTwcyeMrMfzGyzmX1uZreZWXoFbZqa2e1+3c1+2yfNbOftfS5pGNbNm0TRhlVBuemefbAmTYNy8aZ1rJ37QhihSTVo/pCIiEjii9uECLgJuBs4BahUwmBmfwGeAvYFpgPzgGOAd8xsYDltBgOzgN8Bi4HXgT2Bp83s3nLadAYWAucBvwFTgEbAzcB0M0uL0SYdmOF/rky/zXfA+cBCM9ujMp9RwrXmvX8H77P7j6D1KTfQ5tQ7gNKehbXvPxdCZFJV//vf//jvf/8blJUQiYiIJKZ4TojmAHcA/wfsBGypqLKZ9QdG4SUoBzjnBjrnfgccDhQBT5lZVlSbHYAn8ZKZIc65I51zQ4CuwApgtJkdGeNxE4BWwEPOuf2cc8OAvYBJQD/guhhtbgT6+J+ri3NumHOuNzAaaO3HIQ1cx1HPkta+K23PHBvMFUrfuSsteg8ureQcm7//JLwgpVJmzJgRvN99993ZfffdQ4xGRERE6krcJkTOuXucczc756Y6536qRJOr/OOdzrnlEfeZAzwOZAEXRLW5EGgBTHHOTYxo8zPwB784OrKBmR2Ml/SsjKiDc64QuBTYCuSaWWpEmybA5X7xMufchoh29+P1TB1hZj0r8TklRClpzWh39r2kd+hW5nzWoWfQeMeOQfm3aQ+ycePG+g5PqkDD5URERJJD3CZEVWFmTYEcv/hSjCol5wZEnT+xgjavApuB/lHzgkraTHXOlem18hOpd4Fs4NCIS/2AlsAXzrmFVYhP4oSlNmHHE64E8/6TK1z9AzfeeGO4QUm5nHNKiERERJJEUiREeMPV0oBfnHPfx7i+wD/uH3X+gKjrAedcAbAUSAe6VKZNBc+qThuJM2nt96LFwacE5b/85S/Mnj07xIikPMuXL+f770v/V5GTk1NBbREREYlnyZIQ7eIfYyVDOOfygTVAtpk1BzCzFni9NuW2izi/a2WfVYttJA5lHXomqTt0ALxeiPPPP19D5xqgyN6h/fbbjzZt2oQYjYiIiNSlZEmIMv1jRb955vvH5lFtKmoX3aYyz6qtNuUys09ivYBOlWkvdcdSm9DqxN8HQ+eWL1/OTTfdFHJUEk3D5URERJJHsiREIg1G9NC5Bx54QEPnGpDi4mLy8vKCshIiERGRxJYsCVHJqm0ZFdRp5h/XR7WpqF10m8o8q7balMs5t0+sF/BFZdpL3cs69Ey6du0KaOhcQ/Pxxx+zapW3uW6jRo04/PDDQ45IRERE6lKyJETf+scOsS6aWTO8ZbdXO+fWAzjn1gFrK2oXcf6byj6rFttIHLPUJjz11FOkpGjoXEMTOVyuV69etGjRIsRoREREpK4lS0L0Gd7Gra3NbOcY13v4x8VR5xdFXQ+YWWNgX7yltz+vTJsKnlWdNhLn+vTpw+jRpdtYaehcw6D5QyIiIsklKRIi59wmoGTb+aExqgzxj1Ojzr8adT3SSXhLbk93zm2O0WaAmaVFNjCztsBhwGog8jff2Xi9UZ3MrHsV4pM4d/vtt28zdG7Tpk0hR5W8CgoKePfdd4OyEiIREZHElxQJke9+/3ijme1ZctLMDgFG4C27/feoNuOBdcDJZjYook0bYKxfvC+ygXNuHl6C0wa4J6JNKvAo0Bh4yDm3NaJNAfCIXxznD+EraXcV3v5Ds5xzH1XtI0tDl56evs3QOW3YGp65c+cGc7nS09M55JBDQo5IRERE6lrcJkRmdqKZzS15AU3883MjXieW1HfOTQceBHYEPjazyWY2DXgHSAXOd86tiXyGc24VMBwoBl4ysxlm9iLeELzOwP3OuZkxwjsf+A0YZWaLzew5v80g4H3g7hht7gQ+APoCy83sef9z3Qf84schCSjW0Ln3338/xIiSU1FREePHjw/Khx56KOnp6SFGJCIiIvUhbhMioDXQO+Jl/vnIc60jGzjnrsRLVj4FjgEOAaYDhzvnJsd6iHPuZeBw4A3gQOAEYAVwnnNudDltlvt1J/gxnIKXVN0BHO2c2xKjzWbgKL/ORmAg3kasE4AezrkvK/hZSJy77bbb2GuvvQANnQvLggUL+Oc//xmUNVxOREQkOaSGHUB1Oecm4CULdd7OOTcbOL6Kbb7DS76q0mYTcLP/kiTStGlTJkyYQL9+/SguLubzzz/npptu4t577w07tKTx5ptvlikrIRIREUkO8dxDJJJQ+vTpw1VXXRWU77//fg2dqyPOOfLz88u8Jk+eHFxPTU1lr732KnPdORdewCIiIlJnlBCJNCC33367hs7Vg8WLF5OZmVnm9eGHHwbXCwsLadmyZZnrS5YsCTFiERERqStKiEQakKZNm5ZZda5k6JzUrilTplS5TWQPkoiIiCQOJUQiDcwhhxyioXN1LDc3l0GDBm2/om/w4MHk5ubWYUQiIiISFiVEIg2Qhs7Vre5jZvNh5/PJ7j8CUhqVXzEllez+I5jf6TyysrLqLT4RERGpP0qIRBqgkqFzZt5q8p9//jk336zFB2uTmdGi5wCa7nFQuXVa9hlKi54Dgj8HERERSTxKiEQaqOihc/fddx9z5swJMaLEU7RxLZu+mFfu9Q2L38QVbq3HiERERKS+KSESacDuuOMOunTpAmjoXF34ZfIYiFhOO333HmR06RuUizb8xrr5k8IITUREROqJEiKRBix66Nxnn32moXO1pHDtSrZ8V7qUdvpuPWgz9DZaDbzOm1vkW/Pev8MIT0REROqJEiKRBq5v377brDqnoXM1t3rmU8H7lKbNaX3KdZhZMLeo7ZljSWvflY65z4QYpYiIiNQ1JUQicSBy6FxxcbGGztXQ5u+WsvG/7wbl7KMuJKVJ0zJ10jt0o93Z95KS1qy+wxMREZF6pIRIJA5o6FztKS4uZvXbfwvKTXbak2b7HhViRCIiIhImJUQicaJv3778/ve/D8oaOlc9Tz/9NAU/fxGUs3Muxkz/KxQREUlW+i1AJI7ceeedMYfOFRUVkZeXR15eHkVFRSFH2XCtX7+e66+/Pihn7H046R32DjEiERERCZsSIpE40rRpU5588skyQ+duueUWFixYQE5ODjk5OSxcuDDkKBuuu+66i59++gkAS00j+8jzwg1IREREQqeESCTO9OvXr8zQufvuu49//OMfQXnWrFlhhNXgffnll9x///1BucXBg0ht0SbEiERERKQhUEIkEmecc1x77bV06tQJ8IbOPfVU6RLSM2bMID8/v8zLRWw+mqyuueYaCgoKAGiUuSMteg8OOSIRERFpCJQQicSZxYsX06ZNG774onRhgPz8/OD9tGnTyMzMLPNasmRJrFsljZkzZzJx4sSgnHXkeaQ0SQ8xIhEREWkolBCJxJkpU6ZUuc3kyZNrP5A4UVRUxJVXXhmUe/fuTbNuR4QXkIiIiDQoSohE4kxubi6DBg2qdP3BgweTm5tbhxE1bE8++SSLFi0Kyg8++KCW2RYREZGAfisQiTPdx8zmw87nk91/BKSkll8xJZXs/iOY3+k8srKy6i2+hmTt2rXccMMNQfmss86id+/eIUYkIiIiDY0SIpE4ZGa06DmAln2GlFunxUEn06LngGCJ7mR055138ssvvwCQkZHB3XffHXJEIiIi0tAoIRKJU66wgA2L3yz3+roPp1CUv7YeI2pYVqxYwYMPPhiU//jHP9KhQ4cQIxIREZGGSAmRSJxaN28SRRtWBeWmXQ4hNWun0grFhfz4jyspLtgcQnThu/rqq9m6dSsAHTt25Oqrrw45IhEREWmIlBCJxKk17/07eJ/dfwStB17PThc9QZOd9w7OF637hV8m3s6mTZvCCDE0b7/9dpnV+MaOHUtGRkaIEYmIiEhDpYRIJE51HPUsae270vbMscFcoZSUFNqdeQ/pe/QK6m3+ZjGnnHIKmzcnR09RYWFhmWW2+/bty7Bhw8ILSERERBo0JUQicSolrRntzr6X9A7dypw3S6HN4Btpts9Rwbk33niDU089lYKCgvoOs96NHz+epUuXBuW//OUvSb2whIiIiFRMCZFIArKURux4wpVk7HVocG7q1KmcccYZFBYWhhhZ3VqzZg033XRTUD733HPp1atXBS1EREQk2SkhEklQltKIVgOupumefYJzL7/8MmeffTZFRUUhRlZ3br/9dn799VcAmjVrxl133RVyRCIiItLQKSESSWDWKJXW//dHTjjhhODcc889xwUXXEBxcXGIkdW+zz77jIcffjgoX3/99bRv3z7EiERERCQeKCESSXCW2piXX36ZY445Jjj39NNPc8kllyRUUjR69OhgOOCuu+7KVVddFXJEIiIiEg+UEIkkgfT0dCZPnsyRRx4ZnPvb3/5Gbm4uzrnwAqslb7zxBq+++mpQ/vOf/0x6enqIEYmIiEi8UEIkkiQyMjKYOnUqffv2Dc6NGzeOq6++Oq6TosLCwjK9QYcddhhDhgwJMSIRERGJJ0qIRJJIZmYm06ZNK7Py2v33388NN9wQt0nR448/zrJlywAwMx588EEtsy0iIiKVpoRIJMm0bNmSN954gwMPPDA4d/fdd3P77beHGFX1rFq1iltuuSUoDx8+vMznEhEREdkeJUQiSSg7O5s333yT/fbbLzh36623MmbMmBCjqrpbb72VVatWAdC8eXPuvPPOkCMSERGReKOESCRJtWrViunTp9O1a9fg3HXXXccDDzwQYlSV9+mnn/Loo48G5RtuuIF27dqFGJGIiIjEIyVEIkmsTZs2vP3223Tu3Dk4d9VVVzFu3LgQo6qcq666Kthgdo899uDKK68MNyARERGJS0qIRJJc+/btmTFjBrvvvntw7vLLL2f8+PEhRlWxadOm8frrrwfle++9l7S0tBAjEhERkXilhEhE6NixIzNmzKBjx47BuYsvvph//OMfIUYV29atW8sss33UUUcxcODA8AISERGRuKaESEQA2G233ZgxYwbt27cHwDnH+eefz/PPPx9yZGWNGzeOzz77DICUlBQeeOABLbMtIiIi1aaESEQCnTt35u2336Zt27YAFBcXc+aZZzJx4sSgTlFREXl5eeTl5QVzeOrLr7/+ym233RaUL7zwQg444IB6jUFEREQSixIiESmja9euTJ8+nR133BHwEqDTTjuNV155BYAFCxaQk5NDTk4OCxcurNfYbrnlFtasWQNAixYtuOOOO+r1+SIiIpJ4lBCJyDb23Xdfpk+fTlZWFuDN2xk8eDBvvPEGs2bNCupFvq9rS5cu5fHHHw/KN998M23atKm354uIiEhiUkIkIjF1796dN954g+bNmwNQUFDAySefzEsvvRTUmTFjBvn5+WVezrlai6FkeN6MGTO48sorKS4uBryhfVdccUWtPUdERESSV2rYAYhIw5WWlsb69euD8pYtW/jggw+C8rRp08jMzCzTZtGiRey///618vyS4XnR7rvvPpo0aVIrzxAREZHkph4iESnXlClTqtxm8uTJtfb8WEPy+vfvz4ABA2rtGSIiIpLc1EMkIuXKzc1l0aJFZVaZq0h6ejovv/wyH3zwATvttBPt2rWjXbt2wfuSY0ZGxjZtnXNs3LixzLm8vLwyZTPjT3/6U1AvIyNDS26LiIhIjSRVQmRmM4EjKqhyvHPu9RjtzgNGAt2AAmAucKdz7v0KntUPuAHoAzQBlgGPOOfK3enSzDoAdwDHATsA3wLPAnc75zZX9NlE6kL3MbNxnc8nu39rVs/4OxQXVlh/8+bNLF68mMWLF1dYr0WLFmUSpHbt2uGc46GHHqqwnXOO3r17B+XaHJ4nIiIiySmpEqIILwMbYpz/X/QJM/sLMArYBLwJpAPHAMea2RDn3OQYbQYDz+MNSXwH+BU4GnjazPZ3zl0do01nYA7QClgKvAscBNwMHG1mRzvntlT5k4rUkJnRoucAijeuZe37z9XKPdetW8e6dev4/PPPa3SfyZMnKyESERGRGknWhOhq59zX26tkZv3xkqHfgEOcc8v984cAM4GnzGymc25NRJsdgCeBRsBg59xE/3xb4D1gtJm94pybGfW4CXjJ0EPOuVF+m1TgBeAU4Drg1mp9WpEacoUFbFj8ZrnXG2XuQJtTb6d403oePHl3fvzxR3766Sd++umn4P2PP/7Ir7/+Wmur0A0ePJjc3NxauZeIiIgkr2RNiCrrKv94Z0kyBOCcm2NmjwO5wAXAfRFtLgRaAFNKkiG/zc9m9gdgIjAaL6ECwMwOBvoBK4E/RLQpNLNLgZOAXDO70zlX8ZglkTqwbt4kijasCsoZXfoCsPFzb9Ro0YZVbFoxj5aHnMqwYSeWe5+tW7eycuXKmMnShOkfU7j+NwpX/0Dx5vXl3oOUVLJzLmB+p5OCfZJEREREqksJUTnMrClQst7vSzGqvISXEA2gbEJ0YsT1aK8Cm4H+ZpYeMS+opM3U6GFxfiL1rh/LoUQkUiL1Zc17/w7eZ/cfQfMeJwGwfsErrJ7+RFCn5SGnVnifxo0bs/POO7Pzzjtvc+3Va18N3q9+55+sm/N8zHu07DOUFj21ypyIiIjUjmRddvsCM3vUzB4xs1wz2yVGnb2ANOAX59z3Ma4v8I/RExgOiLoecM4V4M0PSge6VKbNdp4lUi86jnqWtPZdaXvmWFr0HICZBXOL2p45lrT2XemY+0ytPMsVFpC/5K1yr29Y/CaucGutPEtEREQkWXuIbowq32tmdzjn7og4V5IkxUqGcM7lm9kaINvMmjvn1ptZC6BlRe388wcBuwIlS3FV+KyI87uWc70MM/uknEudKtNeJFpKWjPanX1vzGvpHbqVe606tj887zfWzZ+03d4oERERkcpIth6id4Cz8RKDDLxeoBuAQuB2MxsVUTfTP5bdGKWsfP/YPKpNRe2i21TmWbHaiCSk6OF5rQZeR6uB15Hdf0TMOiIiIiI1kVQ9RM65m6NOfQ7cZWYfAm8At5rZX51zm+o/utrjnNsn1nm/56hbPYcjUiUdRz3LyhduIeuo4aR3KP3r2qLnAJq07cSavCdpc+ptIUYoIiIiiSTZeohics69CXwIZAEluz6W7FOUUUHTZv6xZEmsyL2NymsX3aYyz4rVRiQhlQzPi0yGSpQMz0tJaxajpYiIiEjVKSEqVbKs9k7+8Vv/2CFWZTNrhpdArXbOrQdwzq0D1lbULuL8NxHnKnxWOW1ERERERKSGlBCVyvaPJfN1PgO2AK3NbNs1gqGHf1wcdX5R1PWAmTUG9sVbevvzyrTZzrNERERERKQGlBABZtYaOMwvLgDw5xHN8M8NjdFsiH+cGnX+1ajrkU7CW3J7esQeRJFtBphZWlRsbf3YVgOzK/4kIiIiIiJSFUmTEJlZXzMbaGaNos7vBkzCm6fzn6g9h+73jzea2Z4RbQ4BRgBrgL9HPWo8sA442cwGRbRpA4z1i5EbueKcm4eX7LQB7olokwo8CjQGHnLOafMVEREREZFalEyrzHUBngJ+MrMFeMnMrkBPvF6bT4CLIhs456ab2YPAKOBjM3sLaAIcAxhwvnNuTVSbVWY2HHgBeMnMZgK/Af3x5hzd75ybGSO+84E5wCgzywGWAb2APYD3gbtr9OlFRERERGQbSdNDBHwAPAb8gJdonIo3n+djYDTQyzm3MrqRc+5KvGTlU7xE6BBgOnC4c25yrAc5514GDsdbyvtA4ARgBXCec250OW2W+3UnAK2BU4Bi4A7gaOfclip/YhERERERqVDS9BA55z4FRlaz7QS8RKUqbWYDx1exzXd4yZeIiIiIiNSDZOohEhERERERKUMJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNJSQiQiIiIiIklLCZGIiIiIiCQtJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStJQQiYiIiIhI0lJCJCIiIiIiSUsJkYiIiIiIJC0lRCIiIiIikrSUEImIiIiISNJSQiQiIiIiIklLCZGIiIiIiCQtJUQiIiIiIpK0lBCJiIiIiEjSUkIkIiIiIiJJSwmRiIiIiIgkLSVEIiIiIiKStP6/vXsPl62u6zj+/ipyN26iIHKJm1oJXkpQMykrS8WwtJIsTxZ/JKmIl+J51I54ycdKMbXshlBaGYSQIWSlmJlXUChv4JGDgqigHOQiR5Jvf/x+0xmHmb1nXWbvffZ6v55nPWvvdfuu38zsz57fmjVr2SGSJEmSNFh2iCRJkiQNlh0iSZIkSYNlh0iSJEnSYNkhkiRJkjRYdogkSZIkDZYdIkmSJEmDZYdIkiRJ0mDZIZIkSZI0WHaIJEmSJA2WHSJJkiRJg2WHSJIkSdJg2SGSJEmSNFh2iCRJkiQNlh0iSZIkSYNlh0iSJEnSYNkhkiRJkjRYdogkSZIkDZYdIkmSJEmDZYdIkiRJ0mDZIZIkSZI0WHaIJEmSJA2WHSJJkiRJg2WHSJIkSdJg2SGSJEmSNFh2iNaYiNglIk6PiCsj4o6I+EpEnBkRB6z2vkmSJEnrjR2iNSQidgbeB7wM2B24APgy8OvAJyPi0FXcPUmSJGndsUO0trwUOBb4MHBkZv5SZh4DvBDYFzhzNXdOkiRJWm/sEK0REbEj8Nv115Mz89bRvMx8PXAF8LiIeMRq7J8kSZK0HtkhWjseA+wBbMrMT06Zf24dH79yuyRJkiStb3aI1o6j6/iyGfNH049agX2RJEmSBiEyc7X3QUBEvB54AfCGzDx1yvyjgU8Bl2XmkqfNRcSnZ8x60E477XSPww47rOvutnbV125dfqEJR9xvd2tZy1rWspa1rGWtQddqU2d7qNWXTZs2sXXr1lsy8/uarmuHaI2IiD8HTgJenZkvnTL/cOAq4KrMPHKZbc3qEB0JfJty5bq1ZtRL22Qtaw2g1npsk7WsZS1rWctaq+lA4PbM3K/pijssYGe0yjLzB1d7H5oadeJWYt+tZa3VrrUe22Qta1nLWtay1vbK7xCtHaPPJnedMX+3Or5lBfZFkiRJGgQ7RGvHl+r4ATPmj6ZfswL7IkmSJA2CHaK14/I6fviM+aPpV6zAvkiSJEmDYIdo7fgQcDNwWEQ8dMr8p9Xxu1dsjyRJkqR1zg7RGpGZ3wHeXH99S0SMvjNERJxKuf/QBzLz0tXYP0mSJGk98rLba0hE7AxcAhwDXA98EDi4/n4DcGxmfnHVdlCSJElaZ+wQrTERsQtwGnAi5Xrq3wQuBl6Wmdeu5r5JkiRJ640dIkmSJEmD5XeIJEmSJA2WHSJJkiRJg2WHSJIkSdJg2SGSJEmSNFh2iCRJkiQNlh0iLVxE7BIRp0fElRFxR0R8JSLOjIgDllhnQ0R8LCJujYhvRsR7IuLRfdaKiAMj4jkRcVZEfDYi7oqIjIjj+mxXRNwjIh4bEa+LiEsj4paI2BoRmyLirRHx/T236ykRcXZE/HdE3BgRd0bE1+tj+OQ+a81Y/9/q45gR8YAe27VxbLvThtf23a6IuFdEnFJfi9+qr8cr51ivSbuWatNoeF9f7YqI+0fEmyPiC/V1eHtEXBERr4iIe/f5GEbE3hHxB2O1vh4R50bEQ5ep84iI+N2IOC8irh09DkutU9drlBtN60SHzGhSKzpmRot2tc6Mts/VxDbmyowW7WqdGR1eg40zo0W7WmdGm3ZFy8xoWattZuwaESdExF9FxOej5NNtEXF5RLw8InZfYt2mudG4VrTIjqZ1okNutGxTp/caa05mOjgsbAB2Bj4MJPAV4J3AR+vvXwcOnbLOGXX+7cD5lPsw3Qn8L3BCX7WAU+q8yeG4PtsFHD627euBC4DzgGvrtG8BP9pju84F7gL+G7gQ+HvgI2P78Jo+n6+J9TfUZe+q4wf02K6Ndd5/AmdNGZ7e8+twb+ATY+ucV4cr6rSpz1mLdk1ry2i4oa73ip5qHVGnJ3A18I/1NXJTnfZpYI+eau0PbGLb6/58yuvwLmAr8NNLPF/nM+Vvc5nX3hk0zI2mdeiWGXPXontmNG1Xl8xo/Fx1yIym7dpI+8xo8xpsmxlN2zWtLaNhucxoWqtLZjSt1SUzfnOsxmeAf6BkwLfqtM8C952y3hk0z43GtWiRHU3r0CE3WrapdW6sxWHVd8BhfQ/Aq+ofxn8Bu49NP7VOv2Ri+Z+s028Ejhib/qgaiDcBe/ZU6ynAGyg3wT0C+JflAqpNLeAw4L3AT1Dv/VWn7wS8rS5/DXCvntr1MGCfKds5BrilBthD+qg1se6+wDfq47iZ5d/cNG3Xxjp9wwq8DgN4X523EdhhYv6hwH36fgwntrMncEdd54ie2nVenf4W4J5j0/dgW2dn1hupprXeXae/B9htbPoJwHcpb9zuPaPW7wCnA8cD+40ehyUeq1a50aJOl8yYuxbdM6Npu7pkRqNaE+s2zYym7dpI+8xoWqtLZrR+DCe2syfLZ0bTdnXJjKa1umTGs4A/Ax48MX1/4LK63b+dmNc2N9rUapwdTevQITdatql1bqzFYdV3wGH9DsCOwJb6h/SwKfMvr/MeMTbtPXXaKVOWf2Od98I+ak1Z5uLlAqqvWmPL7jK2rcctslZd/i/r8s/ruxbwDuDbNZQ3s8Sbm5avjY20eHPTstYv1mn/sOhaS2zrpLrsh3ts14112n5Tln9qnfeerrWAA+vvdwIHz3itJPD8OR/X5d5ItcqNpnWmLD9XZvRRa2y9JTOjz1p13ZmZ0bVWk8xo+bpolRkta7XKjJ5fG0tmRst2tcqMprX6zoyJdR9V170D2HFsei+5MU+tKcu1zo4mdcaWb5wbbWvVdRrlxloY/A6RFukxlKNImzLzk1Pmn1vHx0P5fgLlyMb4vJnLd6nVUW+1MvPbwJX11/svslZ1Zx1/p89aEfEzlCNfr87MTXPsx1p/vk6q4zetQK1ZnlnHf9Njra1z1P1GD7UeXsdXZ+Y1U5Z/fx3/3Bz7s6SOubHdmSMz+rZUZrTWIjPWuraZ0aflMqONtpnR1CIz4/I63gnYBxaaG3ertSCN6nTMjTZtWkhuLNIOq70DWteOruPLZswfTT+qjh9I+YO7ITOvnWP5LrW66K1WRNwDOLj++tUF13oI8EuUoPrXvmpFxG7AnwKfA1633H50qVX9RP2C7c6Uc6MvysxL+6oVEfcCfpRyDvnHIuIo4OnAfYHrgAsy8/JpG2paa5aIOAh4LOW5emePtd5L+c7GyyLieZn53VpvD+AldZkze6i1Wx3fNGP50Ruoo2fMb6JLbmx35siMPmstlxltt9smM7pomhmNdMyMvvZhnsxoo21mNLXIzDi0ju8Evll/XlRuTKu1CI3qdMyNprUWkhuLZodIi3RQHU8Lm/Hpoz/SJZfPzNsiYguwV0TcOzNv6VCriz5rPYPyT/MGynczeqsVEccDvwDcq27n0ZSAOmnGEdm2tU4HDqF89D/v0aAuj+GvTvz+yoj4R8ppMbf2UOtQyhunrwEvAF7N916Rc2NEvDEzX9BDrVl+hfKdhIsyc9bR1za1TgMeATwHeGJEXEpp62Mop0M8MzPfz901rXXDlNrjRlc72jsidp/xvM2rS25sj5bLjNZaZEZbbTKji6aZ0VSXzOjLPJnRRtvMaGqRmfH8Or44M0efeC0qN6bVWoSmdbrkxpK1VjA3FspT5rRIo8s03j5j/m11PLps53LLT1unba0ueqkVEQdSrnAD8PIZodal1tGUL0qeSDl6uRV4LrNPp2hcKyIeTgnLszPzAzPW66UW8AXgRcAP1vUPpLwJuI4Sxn21a6863gf4feCtlO843Af4Dcp3Hk6JiJN7qDXLPKe+NK6VmV8FjqMc9T2E8rg9ifJl7P8CZh01b1rrY5TX2/3qqVH/LyKCcsSZiXXa6pIb25U5M6OLppnRWIfMaKNtZjTVJTP6sojT5bpkRlMLyYyIeCLlObgTeNnYrN5zY4lavWpap0tuzFlr4bmxEuwQSaugnjJyHuUf5vmZ+da+a2TmqzIzKF+mfAjlKjN/DlwQETt23X5E3JPyxcktlDcdC5WZb8/MP8rMz2TmbZl5bWb+LfAjlNMpToiIY3soNcrFHShHW0/OzC9m5jcy80zgxXX+aT3Uupv6hvEHKI/ru3ve9lGU88EfSDkXfy/gAZRLwv4s8KGIeGDXOpl5M/An9dezI+KpEbFH3fbfAw8eW/yurvWGwMxozszoZdvbbWZExIOAt1M+OXvxIk9bXKlaTet0yY15ay06N1aKHSIt0ugj7V1nzB+dMzz6KHq55aet07ZWF51q1fPNzwF+mHJ/jBMXVQsgM+/IzP/JzJMpX/h9MuXoTddap1Auu/mSzLxxVv0Zenu+MvN6SgAD/MyURdq+Dhnb7riz6viAiDi8Y61pRkd6z1nmSF6jWvV1dy7lC7U/n5n/lJlbMvO6zHwj8FLKfVRO71qrOq3Wuy/lH/IWyndGTmDbKRjU6V10yY3tQsPM6KxBZjR1Cu0zozdzZEZTXTKjD/NmRiMdM6ON3jIjyk1wL6Z04F5f93dcb7kxR61eNK3TJTfatGmBubEi7BBpkb5Ux7PuPD6aPrqizJLL1yMdewI3TTmft2mtLlrXql9sPJtyZO1TwPH16i+915ph9BH2tCv1NK11POWyms+KiEvGB8o9JwDOqdMm33T03a6r6nj/KfOa1hqvuXly4cy8nXKjQij/uLvU+h71CPov11/fPmMbbWsdS7n/xdWZOe3iCOfU8Y/1UIvM3JqZT6/bew3wF8DLgR+inH4D8IUe3sB1yY01r0Vm9G2pzGiqS2b0banMaKpLZnTSMDOa6pIZjfWVGRGxd13+YEoHddqnkb3kxpy1Omtap0tu9NSmPnNjRXhRBS3S6OPVh8+YP5p+RR1/nnLu6b4RcUBmXrfM8l1qddGl1psoX268EnhCZm5ZYK1pRkdl9+2pVrD0P8PR6Shn9VBrKaNz+G+bMq9Rrcy8OSKupnyJd6/Jhes/mj3rr5Nf7O3arsdT3qBdA3xwxjJta43+8d88Y/nR9Lu1uUWt/5eZH2SiLRHxa/XHS2Zsr4kuubE9aJoZfVsqM9pomxl9WyozGumYGV01yYymumRGa10yIyJ2By6inEJ4HuWL/Tll0c650aBWJy3rtMqNHtvUd24sXq6BmyE5rM+B772Z40OnzF/UjVnnqjVlmTY3Zp27FvAqtt0p+qBFPYbLbG9DXf6fF1mLZjdm7VorgI/U5Z/Z0+vwjDrtNVOWf3SddzuwU5/tAv66zn9V368N4Mfr77cy5W7vlDdWCXxuBZ6vj9blHznn63aQN2alRWb00a6JdTcwIzP6rLVcZvRca8nMaFOrbWZ0bVeTzGhaq0tmLOD5WjYzKJfR/ve63MUscxPRLrnRtNaU9ed9v9G4Ttvc6NqmiW01yo21MKz6Djis72HsD/NDwG5j00+t0y+ZWP4n6/QbgSPGpj+qhulNwJ591Jqy/txvblq06wV1+vXj7er7MaQcjTkJ2HXKdn6KctpGAr+wiMdwbPnNLPPmpkW7TmbinzLlSkFvHXts79buls/XIZSjh98Cjh2bfh/K1ZASeEufjyHlXPZb6jIPXMBrY3RZ4KScSrHT2Lz7U46Eznxj1eIxPAi478S0XSinwSTwtgZ/A8u9GW2dG03qTFl+YR0iOmRGk1p0zIyuj+HYepvpsUNEx8xo8Xy1zoy2j2GbzGj4GHbKjBaPYevMAO5J+UQjgf+Y53ltmxttak3ZxrLZ0bJNrXKjaa2+c2MtDFF3XlqIiNiZ8hH3MZQ/0A9Szks9hnI9/GMz84sT65xB+QLl7ZSbeu1I+QML4GmZeX4ftSJif+BdY5t4ELAH8FnKPzWACzPzlV1qRbkh4GV1/z/MtrtFT/rLzPzPjrUOAa6mPHaXUu6xsBtwZG0fwBsy89RpO9Dm+Zqxnc11vQNz+k3v2rbrVuDjdfl9Kac17EP59OLJmfmhvtoVEc+mXBHrfynP282UI737UJ7Px+WUe2G0fQwj4kTgHcDHM/OR09rRtVZEnEA5738HyqWHP0F5w/EoyqVle2tXRGygvJH5BOV8/V0o9y7ZG/gX4ITMvGNGu57E917i9ZFsO0o88srMvHBsnTNomBtN63TMjLlr9ZAZTWodQrfMaPxczdjOZpbPjDbtapsZbV6DbTOj1WPYMjOavuZPoH1mNK21gfaZ8Xy2XVr6XWz7e5z0ohy7oEfL3Ghcq012NK3TJTda1DqEDrmxJq12j8xh/Q+UUDudck+IrZR/TG9j6U8PNlBC8TbKUZqLgEf3WYtyRC+XGc7qWotyD4fl6iTlJoFda+1KucTrhZQjrrdTjnRdDfwd83361fj5mrKNzcxxtLdBu+4NvJbyhvza2qbbgP8B/hA4YEGvw+MoR/JuqjU/A/weY5+Q9FhrdPrG8xb590W5ytc7gC8D36G8Yfwk5QpPu/T49/WQ+pq7mnIfli2UTtSzoRyMW+bvv/HfCw1zo2kdOmRGk1p0zIyGtTplRtvnqk1mNGxXp8xo2y5aZEaHWo0zo00tWmZG01p0y4yNc9RK4JAecqNxLVpkR9M6dMiNFrU6v9dYa4OfEEmSJEkaLC+7LUmSJGmw7BBJkiRJGiw7RJIkSZIGyw6RJEmSpMGyQyRJkiRpsOwQSZIkSRosO0SSJEmSBssOkSRJkqTBskMkSZIkabDsEEmSJEkaLDtEkiRJkgbLDpEkadAiIiMil1lmQ13urBXaLUnSCrFDJEmSJGmw7BBJkiRJGiw7RJIkSZIGyw6RJEkdRMQOEfHciLg0Im6tw8ci4rci4p5Tlt886ztLEXHctO8qRcRZdfpxEfGEiHh/RGyp0/ZcTMskaRjsEEmS1FLt8FwA/DFwOPCvwL8BDwL+BDgnIvr8X3sicBGwWx1/HFjyghCSpKXtsNo7IEnSduwU4InAp4HHZ+bXACJif+D9wFOB5wBv7qneScAvZ+Y7e9qeJA2enxBJksS2y29PG4C3zVjteXV86qgzBJCZ1wMvrr8+v8fdvNDOkCT1y0+IJEkqzl5i3uHAY8YnRMRBwEHADZn53inr/DOwBTg8IvbLzK/2sI//1MM2JElj7BBJkgRk5oZZ8yJiAxMdIuD+dXzNjO1lRFwD7AkcAPTRIfpSD9uQJI3xlDlJkhan6QUPlvu/fEfbHZEkTWeHSJKkdr5Sxwcvscxo3nVj074DEBG7T1n+wB72S5LUgB0iSZJayMwvUU5h2zciHj85PyKeBOwFfGHi+0PX1/GRUzb7U73vqCRpSXaIJElq7011/PqI2Hc0MSL2A/6g/vrGiXU+UMenjd+4NSKeATxjUTsqSZrODpEkSe29gXKD1KOAqyLivIh4F3Al8GDgfMoNWse9BbgBeBrwmYg4JyI+BfwNd+88SZIWzA6RJEktZeZ3gadQ7jX0ReAJwE8DnwdOBp6WmXdNrPM14Mcol+XeH/hZ4GbK6XJeVluSVlhkNr0AjiRJkiStD35CJEmSJGmw7BBJkiRJGiw7RJIkSZIGyw6RJEmSpMGyQyRJkiRpsOwQSZIkSRosO0SSJEmSBssOkSRJkqTBskMkSZIkabDsEEmSJEkaLDtEkiRJkgbLDpEkSZKkwbJDJEmSJGmw7BBJkiRJGiw7RJIkSZIGyw6RJEmSpMGyQyRJkiRpsOwQSZIkSRqs/wNxD5CJ1UMTcwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "plt.plot(ODcount['Hour'],ODcount['Count'],'k-*')\n",
    "plt.bar(ODcount['Hour'],ODcount['Count'],width=0.5)\n",
    "plt.title('Hourly Oder Volume',)\n",
    "plt.xlabel('Hour',)\n",
    "plt.ylabel('Count')\n",
    "plt.ylim(0,35000)\n",
    "\n",
    "#保存图片\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>VehicleNum</th>\n",
       "      <th>Stime</th>\n",
       "      <th>SLng</th>\n",
       "      <th>SLat</th>\n",
       "      <th>ELng</th>\n",
       "      <th>ELat</th>\n",
       "      <th>Etime</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:03:23</td>\n",
       "      <td>114.167465</td>\n",
       "      <td>22.562468</td>\n",
       "      <td>114.225235</td>\n",
       "      <td>22.552750</td>\n",
       "      <td>00:10:48</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22223</td>\n",
       "      <td>01:01:14</td>\n",
       "      <td>114.135414</td>\n",
       "      <td>22.575933</td>\n",
       "      <td>114.166748</td>\n",
       "      <td>22.608267</td>\n",
       "      <td>01:08:17</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0       22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1       22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2       22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3       22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4       22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "\n",
       "      Etime Hour  \n",
       "0  00:10:48   00  \n",
       "1  00:15:19   00  \n",
       "2  00:29:06   00  \n",
       "3  00:54:42   00  \n",
       "4  01:08:17   01  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TaxiOD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "TaxiOD = TaxiOD.loc[0:464716]\n",
    "TaxiOD['st_time'] = TaxiOD['Stime'].apply(lambda x : int(x[0:2]))*3600 + TaxiOD['Stime'].apply(lambda x : int(x[3:5]))*60 + TaxiOD['Stime'].apply(lambda x : int(x[6:8]))*1\n",
    "TaxiOD['et_time'] = TaxiOD['Etime'].apply(lambda x : int(x[0:2]))*3600 + TaxiOD['Etime'].apply(lambda x : int(x[3:5]))*60 + TaxiOD['Etime'].apply(lambda x : int(x[6:8]))*1\n",
    "TaxiOD['last_time'] = (TaxiOD['et_time'] - TaxiOD['st_time']) / 60\n",
    "TaxiOD['Hour'] = TaxiOD['Hour'].apply(lambda x: int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>00:03:23</td>\n",
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       "      <td>22.562468</td>\n",
       "      <td>114.225235</td>\n",
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       "      <td>00:10:48</td>\n",
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       "      <td>648</td>\n",
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       "      <th>1</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:11:33</td>\n",
       "      <td>114.227150</td>\n",
       "      <td>22.554167</td>\n",
       "      <td>114.229218</td>\n",
       "      <td>22.560217</td>\n",
       "      <td>00:15:19</td>\n",
       "      <td>0</td>\n",
       "      <td>693</td>\n",
       "      <td>919</td>\n",
       "      <td>3.766667</td>\n",
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       "      <th>2</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:17:13</td>\n",
       "      <td>114.231354</td>\n",
       "      <td>22.562166</td>\n",
       "      <td>114.255798</td>\n",
       "      <td>22.590967</td>\n",
       "      <td>00:29:06</td>\n",
       "      <td>0</td>\n",
       "      <td>1033</td>\n",
       "      <td>1746</td>\n",
       "      <td>11.883333</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22223</td>\n",
       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
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       "      <td>2205</td>\n",
       "      <td>3282</td>\n",
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       "      <th>4</th>\n",
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       "      <td>01:08:17</td>\n",
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       "      <td>3674</td>\n",
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       "      <td>7.050000</td>\n",
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       "    <tr>\n",
       "      <th>464712</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:08:22</td>\n",
       "      <td>113.913734</td>\n",
       "      <td>22.531366</td>\n",
       "      <td>113.997284</td>\n",
       "      <td>22.545650</td>\n",
       "      <td>22:36:53</td>\n",
       "      <td>22</td>\n",
       "      <td>79702</td>\n",
       "      <td>81413</td>\n",
       "      <td>28.516667</td>\n",
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       "    <tr>\n",
       "      <th>464713</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:39:12</td>\n",
       "      <td>114.005547</td>\n",
       "      <td>22.548067</td>\n",
       "      <td>113.996330</td>\n",
       "      <td>22.537083</td>\n",
       "      <td>22:46:25</td>\n",
       "      <td>22</td>\n",
       "      <td>81552</td>\n",
       "      <td>81985</td>\n",
       "      <td>7.216667</td>\n",
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       "    <tr>\n",
       "      <th>464714</th>\n",
       "      <td>36947</td>\n",
       "      <td>22:49:38</td>\n",
       "      <td>113.994598</td>\n",
       "      <td>22.535049</td>\n",
       "      <td>113.922485</td>\n",
       "      <td>22.496550</td>\n",
       "      <td>23:13:15</td>\n",
       "      <td>22</td>\n",
       "      <td>82178</td>\n",
       "      <td>83595</td>\n",
       "      <td>23.616667</td>\n",
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       "    <tr>\n",
       "      <th>464715</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:24:24</td>\n",
       "      <td>113.921082</td>\n",
       "      <td>22.513483</td>\n",
       "      <td>113.929817</td>\n",
       "      <td>22.494217</td>\n",
       "      <td>23:30:32</td>\n",
       "      <td>23</td>\n",
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       "      <td>84632</td>\n",
       "      <td>6.133333</td>\n",
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       "      <th>464716</th>\n",
       "      <td>36947</td>\n",
       "      <td>23:37:09</td>\n",
       "      <td>113.927635</td>\n",
       "      <td>22.512568</td>\n",
       "      <td>113.910965</td>\n",
       "      <td>22.487867</td>\n",
       "      <td>23:49:10</td>\n",
       "      <td>23</td>\n",
       "      <td>85029</td>\n",
       "      <td>85750</td>\n",
       "      <td>12.016667</td>\n",
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       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "4            22223  01:01:14  114.135414  22.575933  114.166748  22.608267   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464712       36947  22:08:22  113.913734  22.531366  113.997284  22.545650   \n",
       "464713       36947  22:39:12  114.005547  22.548067  113.996330  22.537083   \n",
       "464714       36947  22:49:38  113.994598  22.535049  113.922485  22.496550   \n",
       "464715       36947  23:24:24  113.921082  22.513483  113.929817  22.494217   \n",
       "464716       36947  23:37:09  113.927635  22.512568  113.910965  22.487867   \n",
       "\n",
       "           Etime  Hour  st_time  et_time  last_time  \n",
       "0       00:10:48     0      203      648   7.416667  \n",
       "1       00:15:19     0      693      919   3.766667  \n",
       "2       00:29:06     0     1033     1746  11.883333  \n",
       "3       00:54:42     0     2205     3282  17.950000  \n",
       "4       01:08:17     1     3674     4097   7.050000  \n",
       "...          ...   ...      ...      ...        ...  \n",
       "464712  22:36:53    22    79702    81413  28.516667  \n",
       "464713  22:46:25    22    81552    81985   7.216667  \n",
       "464714  23:13:15    22    82178    83595  23.616667  \n",
       "464715  23:30:32    23    84264    84632   6.133333  \n",
       "464716  23:49:10    23    85029    85750  12.016667  \n",
       "\n",
       "[464717 rows x 11 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TaxiOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>00:15:19</td>\n",
       "      <td>0</td>\n",
       "      <td>693</td>\n",
       "      <td>919</td>\n",
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       "      <td>00:17:13</td>\n",
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       "      <td>22.562166</td>\n",
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       "      <td>1033</td>\n",
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       "      <td>00:36:45</td>\n",
       "      <td>114.240196</td>\n",
       "      <td>22.563650</td>\n",
       "      <td>114.119965</td>\n",
       "      <td>22.566668</td>\n",
       "      <td>00:54:42</td>\n",
       "      <td>0</td>\n",
       "      <td>2205</td>\n",
       "      <td>3282</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
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       "      <td>00:03:12</td>\n",
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       "      <td>22.555000</td>\n",
       "      <td>114.033501</td>\n",
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       "      <td>00:03:43</td>\n",
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       "      <th>464571</th>\n",
       "      <td>36944</td>\n",
       "      <td>00:40:37</td>\n",
       "      <td>114.088852</td>\n",
       "      <td>22.543249</td>\n",
       "      <td>114.116432</td>\n",
       "      <td>22.546417</td>\n",
       "      <td>00:45:11</td>\n",
       "      <td>0</td>\n",
       "      <td>2437</td>\n",
       "      <td>2711</td>\n",
       "      <td>4.566667</td>\n",
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       "    <tr>\n",
       "      <th>464572</th>\n",
       "      <td>36944</td>\n",
       "      <td>00:54:08</td>\n",
       "      <td>114.119980</td>\n",
       "      <td>22.544434</td>\n",
       "      <td>114.067680</td>\n",
       "      <td>22.522182</td>\n",
       "      <td>01:04:49</td>\n",
       "      <td>0</td>\n",
       "      <td>3248</td>\n",
       "      <td>3889</td>\n",
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       "      <th>464621</th>\n",
       "      <td>36945</td>\n",
       "      <td>00:50:15</td>\n",
       "      <td>113.931419</td>\n",
       "      <td>22.522150</td>\n",
       "      <td>113.926498</td>\n",
       "      <td>22.489317</td>\n",
       "      <td>01:01:03</td>\n",
       "      <td>0</td>\n",
       "      <td>3015</td>\n",
       "      <td>3663</td>\n",
       "      <td>10.800000</td>\n",
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       "      <th>464667</th>\n",
       "      <td>36947</td>\n",
       "      <td>00:04:44</td>\n",
       "      <td>113.926964</td>\n",
       "      <td>22.536949</td>\n",
       "      <td>113.892982</td>\n",
       "      <td>22.562799</td>\n",
       "      <td>00:14:36</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>876</td>\n",
       "      <td>9.866667</td>\n",
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       "      <th>464668</th>\n",
       "      <td>36947</td>\n",
       "      <td>00:34:26</td>\n",
       "      <td>113.916664</td>\n",
       "      <td>22.543083</td>\n",
       "      <td>113.920013</td>\n",
       "      <td>22.493601</td>\n",
       "      <td>00:42:48</td>\n",
       "      <td>0</td>\n",
       "      <td>2066</td>\n",
       "      <td>2568</td>\n",
       "      <td>8.366667</td>\n",
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       "  </tbody>\n",
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       "<p>20312 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "        VehicleNum     Stime        SLng       SLat        ELng       ELat  \\\n",
       "0            22223  00:03:23  114.167465  22.562468  114.225235  22.552750   \n",
       "1            22223  00:11:33  114.227150  22.554167  114.229218  22.560217   \n",
       "2            22223  00:17:13  114.231354  22.562166  114.255798  22.590967   \n",
       "3            22223  00:36:45  114.240196  22.563650  114.119965  22.566668   \n",
       "25           22224  00:03:12  114.035164  22.555000  114.033501  22.552917   \n",
       "...            ...       ...         ...        ...         ...        ...   \n",
       "464571       36944  00:40:37  114.088852  22.543249  114.116432  22.546417   \n",
       "464572       36944  00:54:08  114.119980  22.544434  114.067680  22.522182   \n",
       "464621       36945  00:50:15  113.931419  22.522150  113.926498  22.489317   \n",
       "464667       36947  00:04:44  113.926964  22.536949  113.892982  22.562799   \n",
       "464668       36947  00:34:26  113.916664  22.543083  113.920013  22.493601   \n",
       "\n",
       "           Etime  Hour  st_time  et_time  last_time  \n",
       "0       00:10:48     0      203      648   7.416667  \n",
       "1       00:15:19     0      693      919   3.766667  \n",
       "2       00:29:06     0     1033     1746  11.883333  \n",
       "3       00:54:42     0     2205     3282  17.950000  \n",
       "25      00:03:43     0      192      223   0.516667  \n",
       "...          ...   ...      ...      ...        ...  \n",
       "464571  00:45:11     0     2437     2711   4.566667  \n",
       "464572  01:04:49     0     3248     3889  10.683333  \n",
       "464621  01:01:03     0     3015     3663  10.800000  \n",
       "464667  00:14:36     0      284      876   9.866667  \n",
       "464668  00:42:48     0     2066     2568   8.366667  \n",
       "\n",
       "[20312 rows x 11 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hour = 0\n",
    "TaxiOD[TaxiOD['Hour']==hour]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_data = []\n",
    "for i in range(24):\n",
    "    tem_list =TaxiOD[TaxiOD['Hour']==i]['last_time'].to_list()\n",
    "    y_data.append(tem_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(dpi=150)\n",
    "plt.boxplot(y_data)\n",
    "\n",
    "plt.xlabel('hour')\n",
    "plt.ylabel('last_time')\n",
    "plt.ylim(0,60)\n",
    "\n",
    "#保存图片\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "4ce0e62306dd6a5716965d4519ada776f947e6dfc145b604b11307c10277ef29"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
